definition of thesis in music

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Postgraduate Research in Music: A Step-by-Step Guide to Writing a Thesis

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Postgraduate Research in Music: A Step-by-Step Guide to Writing a Thesis is an essential text for music students who are undertaking postgraduate research. Unique in its approach and scope, this is a “how to” book, a practical guide that sets out, step-by-step, how to write a thesis. It discusses all key aspects of the research process in the order in which they are encountered, from the initial stages of a research project to completion of a thesis. It also offers a music-specific focus, with explanations and examples that are immediately relevant for all music research and which take into account the special characteristics of music as a discipline. Lastly, it provides a teaching framework for lecturers. All key concepts are illustrated with music-relevant examples. Exercises, and in some chapters class seminar topics as well, are included to reinforce the concepts being discussed. Reading lists are appended at the end of most chapters, enabling students to explore topics in greater depth. Valuable supplementary information, such as referencing examples, is provided in the appendices. Postgraduate Research in Music is based on the premise that there are certain principles that underpin good scholarship, regardless of the area in which the research is conducted. In distilling and discussing these principles, this book speaks to all scholars working within the discipline of music.

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Petersen Voice Studio

Understanding Arsis and Thesis in Music

James Thurmond’s book Note Grouping changed my life. It is a very small book (it can be read in around an hour), but its principles and brilliance last long after you finish the last page. Since reading this book several years ago, I have never looked at music in the same way.

Thurmond describes the concept of Arsis (upbeat) and Thesis (downbeat) in music. He takes these terms from the ancient Greeks, who used the raising (upbeat) and lowering (downbeat) of the foot for the timing of the Greek chorus.

By emphasizing the notes on upbeats (in a 4/4 measure this would be the second and fourth beats of the bar) the performer gives greater momentum and forward motion to the musical line.

Greater expressiveness requires this emphasis on the upbeat or Arsis. It can be done either by lengthening the note somewhat OR by stressing it dynamically. When music STARTS on a Thesis/strong beat, there is always an understood Arsis . For the singer, this Arsis is the intake of breath – which should be taken rhythmically and expressively in anticipation of the sung text.

Many times singers are told to sing with ‘more line’ or ‘more legato’ but aren’t told in most cases how to accomplish this. A singer that understands Arsis and Thesis in music making can render any song in a musically ‘moving’ way. The singer can break apart the music, understanding the ebb and flow of every phrase, and give each an expression that far outpaces ‘singing the notes.’

When you understand Arsis and Thesis, you begin to understand that the arrangement of upbeat/downbeat reflects so many patterns of duality in our lives. The first act of life is an intake of breath (Arsis). Our last act here is the final sigh of death (Thetic). Everything that comes between is the music of our lives.

In popular and contemporary music, there is a very strong emphasis on the FIRST and THIRD beat. This is in no small part due to the fact that much of this music is connected to dance forms, which require a highly distinct pulse. Classical music requires a different approach to rhythm.

Musical notation exists for ease of READING and is structured from Strong (Thetic) to Weak (Arsic) structure. However, note grouping requires the singer to re-group what they see into more cohesive patterns (Arsis/Thesis). Written in an Arsis/Thesis structure, it would be very difficult to read .

Arsis/Thesis can lead the way to more successful music making, and performances of rare expressive and emotional quality.

In a future post, I will explore Arsis/Thesis within the framework of a classical aria and a musical theater song. The reader will gain a greater understanding of how their inherent musical properties can be brought out from a rhythmic perspective and lead to more expressive performance.

A Dictionary of Music and Musicians/Arsis and Thesis

From volume 1 of the work.

When applied to beating time, arsis indicates the strong beat, and thesis the weak: for the ancients beat time in exactly the reverse way to ours, lifting the hand for the strong beat and letting it fall for the weak, whereas we make the down beat for the strong accents, and raise our hand for the others.

When applied to the voice, a subject, counterpoint, or fugue, are said to be 'per thesin,' when the notes ascend from grave to acute; 'per arsin' when they descend from acute to grave, for here again the ancient application of the ideas of height or depth to music was apparently the reverse of our own.

[ F. A. G. O. ]

DMM articles about musical terms
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Honors Thesis in Music

An opportunity for expression and exploration.

The honors thesis in music allows the student to explore their creative and academic interests at an advanced level. In addition to research-based theses, the Harvard Department of Music supports performance-based theses, compositions, recordings, and creative projects that motivate the student to expand their relationship with the arts. Students are encouraged to think imaginatively and work with their advisors in developing their thesis topic, making use of the department resources available.

Honors thesis projects follow a timeline that begins during the student’s junior year, culminating in their senior year with a final thesis project. Students wishing to pursue the honors thesis track should consider the Thesis Timeline and Guidelines carefully and understand their responsibility for meeting the deadlines described. Music Department advisors and administrators will work closely with students in navigating these benchmarks and will provide regular updates on upcoming deadlines.

Students pursuing a joint concentration are required to complete an honors thesis, researching a topic that is inclusive of both concentrations. This option requires approval from both concentrations. Not all concentrations participate in joint concentrations . Students should schedule advising appointments with both departments before proceeding. For more information, please refer to the Joint Concentration information with the Registrar’s Office .

Thesis Categories

The Music Department supports a variety of these types that allow students to showcase their artistic interests and talents. The categories below are designed to provide guidance and define support levels and requirements associated with each thesis type. Students are encouraged to be creative in their thesis planning, and to consider projects that either combine the definitions below, or expand beyond them.

A research-based thesis that culminates in a written academic document.

Written research-based academic document.

A thesis based on original composed works, showcasing your compositional and creative voice. The student may include recordings (live or computer generated,) but these are not required. Fully drafted scores must be submitted for final review.

Approval by DUS and thesis advisor on proof of concept.
Composition pre-screening requirements as listed below.
Written summary of compositional concept, inspiration, or other conclusions.
Portfolio of fully drafted scores.

A thesis centered on a final recorded product. This may take the form of a recording session, concept album, podcast, or other unique format, and may be paired with original compositions as explained above. The final recording should showcase the student’s skill, technique, creativity, and relationship to music.

Approval by UPC and event management on production needs.
Recording project requirements as described below.
Written summary of recording concept, inspiration, or other conclusions.
Full recording of final project.
Venue access for recording space.*
Recording engineering support.*

A performance-based thesis designed to showcase the student’s instrumental or vocal talent. This thesis takes the form of a typical performance recital, requiring an advanced program of works that highlight the student’s skill and experience. This format assumes a traditional performance setting with small to medium production requirements. A final recording is required.

A statement of support from the student’s priavte teacher recommending the student’s program for a thesis recital.
Approval by UPC and Manager of Events on production needs.
Written summary of program choices, relationship with instrument/voice and program of study, or other summary of experience.
Formal performance of program in a professional performance setting, including secondary performers, collaborative artists, and ensembles.
Full recording of performance.
Venue access for performance space.*
Performance staffing support.*
Recording engineering support*.

We invite students to define a thesis project that is guided by the student’s interests, aptitudes, experiences, and creativity. This format assumes (but does not necessarily require) the need for performance space, recording services, and production engineers. A thesis of this kind might include many, or even all the concepts listed above, and may culminate in a self-composed, self-performed event that includes audio/visual presentations and/or other advanced needs. It may also result in a fully recorded end-product. Students pursuing this thesis type should feel free to suggest creative elements that expand beyond these boundaries. Students who opt to pursue a creative thesis will need to work closely with their thesis advisor, the DUS, the UPC, and the Manager of Events to ensure that their thesis will be achievable within the Thesis Timeline , based on the resources available.*

Approval by UPC and Manager of Events on production needs.
Additional approvals, based on creative elements.
Written summary of creative concept, including discussion of the creative elements included in the final product.
Where appropriate, a formal performance of works in a professional setting, including secondary performers, collaborative artists, and ensembles.
Where appropriate, a full recording of performance event and/or final product.
Support materials (as needed) in the form of audio/visual information, presentation slides, written materials, etc.
Other deliverables as defined by creative elements.
Venue access for performance space (if needed.)*
Performance staffing support (if needed.)*
Recording engineering support (if needed.)*

Thesis Proposal

Due in April of the student’s junior year, the honors thesis requires the student to submit a detailed proposal describing their project concept. This proposal must include the thesis title, type of thesis, and a detailed description. Proposal descriptions should establish the rationale for research, define proof-of-concept for creative events, and begin to identify basic support needs where appropriate. The student must also finalize their faculty thesis advisor at this time, and both the advisor and the DUS are required to sign off on this proposal to approve the student’s thesis plans. If the student is pursuing a joint thesis, then a signature from the advisor in both departments is required, and a Primary concentration and Allied concentration must be identified. The topic of a joint thesis must be inclusive of both concentrations. ( More information on Joint Concentrations .) Before a proposal is submitted, students are required to meet all pre-screening requirements as described in the Thesis Timeline and Guidelines .

2024-25 Thesis Proposal Form [PDF]

The thesis proposal is due to the Music Department in April of the student’s junior year, based on the dates described in the Thesis Timeline and Guidelines .

Thesis Prospectus

Due in September of the student’s senior year, students are required to submit a thesis prospectus. The prospectus should be a significant expansion of the initial proposal and should include a detailed summary of what you hope to accomplish through further research or creation. It should outline goals, purpose, and the scope of your work, and it should describe your methodology and critical approach. It should also establish your project framework and describe the timeline to project completion. Where applicable it should include a bibliography, cited references, or examples.

Students should work closely with their thesis advisor to define prospectus definitions that align with their specific thesis type.

The thesis prospectus is due to the thesis advisor in September of the student’s senior year, based on the dates described in the Thesis Timeline and Guidelines .

Thesis Timeline and Guidelines

It is the responsibility of the student to follow the timeline, deadlines, and guidelines listed below as they navigate their thesis project. It is important to maintain close contact with thesis advisors, the Director of Undergraduate Studies, the Undergraduate Program Coordinator, and the Manager of Events throughout this process.

NOTE TO OFF-CYCLE STUDENTS WISHING TO PURSUE THE HONORS THESIS TRACK : please reach out to the Undergraduate Program Coordinator to receive off-cycle thesis timeline and deadline dates.

IMPORTANT NOTE FOR JOINT CONCENTRATORS: Thesis deadlines may vary between departments. The Allied department will typically defer to the Primary department with regard to all deadlines, requirements, and formats. Students should schedule advising appointments with both departments before proceeding.

All current juniors are required to attend this event to receive an overview of the honors thesis process, requirements, and timeline. Students will be introduced to the music faculty and will gain a basic understanding of the thesis advising process. This event will take place on Tuesday, October 17, 2023, 3pm-4pm . Location to be announced. Attendance at this event is required for all current juniors, regardless of honors thesis plans. Your spring semester advising hold will not be released unless you have participated in this event. If you are unable to attend for any reason, it is the student’s responsibility to schedule an advising appointment with the DUS to review thesis details before your hold will be lifted.

Students wishing to pursue performance, recording, composition, or creative theses must submit to pre-screening to determine readiness to continue.

Students are required to schedule a meeting with the Undergraduate Program Coordinator in advance of the deadline above to review pre-screening requirements and to submit a formal description for evaluation.

Additonal pre-screening requirements are listed below:

Students must have taken at least one advanced composition course within the Music Department prior to beginning their thesis trajectory.
Students must submit a portfolio of recent work for consideration by the composition faculty. Portfolio should consist of 3–5 compositions showcasing your compositional and creative voice. Compositions must be submitted in score form. Students may submit live or computer-generated recordings in addition to your scores, but not in lieu of them. If students have audio-only electronically produced works, audio or digital files may be submitted via your preferred software platform, accompanied by a short statement outlining any compositional technique and/or technology used and inspiration behind the composition.
A description of anticipated recording needs including instrumentation, ensemble size, and musical styles.
A general expectation for audio engineering and editing needs.
A description of venue needs or preferred location for recording.
A resume of training and performance history (including music coursework.)
An audition video of at least 15 minutes with three contrasting pieces.
Where appropriate, a letter from the student’s primary teacher recommending the student’s program for a thesis recital.
If composition related, see Composition pre-screening above.
If recording related, see Recording pre-screening above.
If performance related, see Performance/Recital pre-screening above.
For all other projects, it is required that you meet with the Undergraduate Program Coordinator to review pre-screening expectations based on your project expectations.

Students will be reminded about the upcoming April deadline for thesis proposals and will be prompted to connect with thesis advisors, if they have not already done so. If students have not confirmed a thesis advisor by this date, then they should schedule an advising session with the DUS to strategize.

Students who have been successfully pre-screened for performance, composition, creative, or recording-based theses must submit any significant changes to their plans at this time.

All thesis proposals must be submitted to the Undergraduate Program Coordinator by this deadline using the provided Thesis Proposal Form [PDF] . Thesis advisors must be confirmed at this time, and both advisor and DUS must sign off on student proposals. Students considering a creative, performance, composition, or recording-based thesis must have met all pre-screening approval requirements to continue with a thesis in these categories.

Students must register for MUS99 Senior Tutorial for both semesters of their senior year.

2024-25 Thesis Proposal Form [PDF]

Thesis candidate is required to submit a detailed prospectus to their thesis advisor by this date.

A reminder that students must register for MUS99 Senior Tutorial for both semesters of their senior year.

Students are required to schedule a meeting with the Undergraduate Program Coordinator and the Manager of Events prior to this date to confirm production support needs and venue confirmation for performance-track or recording-based theses. Students are required to submit a detailed Tech Rider at this time to formally request support services.

Students are required to submit 50% of their final thesis to their thesis advisor. Students should coordinate with their thesis advisor to confirm requirements for all non-written thesis elements.

Students must communicate any significant updates for production needs with the Undergraduate Program Coordinator and Manager of Events in advance of this date. Any updates to the Tech Rider must be finalized at this time. No further production updates will be permissible without the express consent of the Manager of Events.

A complete thesis draft must be submitted to advisors by this date. Students should coordinate with their thesis advisor to confirm draft requirements for all non-written thesis elements.

All theses must be completed on or before this deadline for both performance and non-performance track theses. All final documents, support materials, and/or digital media must be completed and submitted by email to the Undergraduate Program Coordinator . All performance-based events must be complete by this deadline. All theses that include a recorded element (including final editing and engineering , ) must be completed and submitted by email to the Undergraduate Program Coordinator before this deadline.

All thesis candidates are required to take part in the Thesis Colloquium. This event is an opportunity for the student to present a summary of their thesis to Harvard’s music community and to celebrate their well-earned achievements with their colleagues. Taking place at the Learning Lab at the Derek Bok Center for Learning , students are encouraged to consider how your thesis can be presented using this creative space.

All final thesis revisions and updates are due to the Music Department by this date and should be submitted via email to the Undergraduate Program Coordinator . This includes all final support materials, recordings, edits, or updates. No further updates will be accepted after this date.

WINTER BREAK Students are strongly advised to use the winter break period to make significant progress on their thesis work in advance of spring semester deadlines.

23-24 Thesis Timeline (PNG)

Thesis Production Acknowledgement

All theses requiring any form of department production support or resources as defined above must provide the following information and/or adhere to the guidelines set below. A request for production needs should be sent to the Undergraduate Program Coordinator by submitting a comprehensive Tech Rider that outlines detailed production needs and expectations.

Students commit to meeting these requirements and/or working within the established guidelines. All thesis support will be provided at the discretion of the Department of Music.

Thesis performances of any kind are limited to 90 minutes total, including intermission.
Performances, recordings, or any theses requiring venues, services, and staffing must take place during the dates and times offered by the Music Department at the time of the Support Approval and Venue Deadline (November.) Event dates must be confirmed in advance and approved by thesis advisors and DUS prior to the point of scheduling.
Venues, equipment, and production support and services are offered at the discretion of the Music Department. It is the student’s responsibility to articulate their needs to the best of their ability by submitting a Tech Rider where necessary and adhering to the deadlines for information as defined in the established Thesis Timeline and Guidelines . Students may opt to supplement department resources with external funding. External funding is the responsibility of the student.

Performances, recordings, or any theses requiring venues, services, and staffing require the student to submit a Tech Rider to the Undergraduate Program Coordinator for evaluation by the Music Department. The Tech Rider should include the following information with as much detail as possible:

Venue requirements and/or preferences.
Ensemble instrumentation.
Stage diagram, including both performers and equipment.
NOTE: Extended techniques, prepared piano, atypical tunings, and other modifications (including removal of lid) are offered only at the discretion of Music Department staff. These atypical needs should not be assumed. Please visit Piano Services for more information.
Audio/visual recording needs.
Audio amplification needs.
Visual projection needs.
Other details as required.

Support services are available in the areas below at the discretion of the Music Department, based on resource availability, and based on the guidelines provided above. Requests for the services below should not be assumed. All requests for support be included in the student’s Tech Rider . All thesis support will be provided at the discretion of the Department of Music. Available department resources include:

Audio/visual recording services or staffing.
Recording editing or engineering.
Audio amplification.
Video projection.
Front of house and/or stage crew for public performances.
Use of piano.

*A Word About Thesis Support Levels…

The Music Department is committed to supporting theses that require department resources in the areas of venue access, equipment use, staffing services, recording services, and audio/visual engineering. Thesis support must be carefully coordinated with Music Department administration for approval, based on the Thesis Timeline and Guidelines , and based on available resources. All thesis support will be provided at the discretion of the Department of Music.

Students interested in pursuing recording, performance, or creative theses of any kind commit to working closely with Music Department administration to ensure that any department resources needed are reasonable, clearly articulated, and approved by the department in advance. Students may opt to supplement department resources with external funding. External funding is the responsibility of the student. Students agree to meet the terms described in the Thesis Timeline and Guidelines .

Loeb Music Library Research Support

Librarians with the Loeb Music Library are available to provide research assistance for student thesis projects. During a one-on-one research consultation, librarians can assist with defining topics, developing search strategies, identifying and locating new sources, organizing research, working with unique materials, and obtaining items external to Harvard.

Students are encouraged to sign up for a one-on-one research consultation here . For further questions, please contact Kerry Masteller .

Harvard Library
Research Guides
Faculty of Arts & Sciences Libraries

Music Research Guide

Dissertations and theses.

HOLLIS (Library catalog and more)
Dictionaries, Encyclopedias, Bibliographies
Recordings and Videos
Scores and Sheet Music
Concert Reviews
Collected Works Editions
Music in Special Collections
Ethnomusicology Research @ Harvard
Jazz Research @ Harvard This link opens in a new window
American Indigenous Music
Senior Thesis Style Guide: Footnotes
Senior Thesis Style Guide: Bibliography
Multimedia Production Resources
ProQuest Dissertations and Theses Global (AKA Dissertation Abstracts) Full text of graduate works added since 1997, along with selected full text for works written prior to 1997 and citations for dissertations and theses dating from 1743-present.
DASH (Digital Access to Scholarship at Harvard) A central, open-access repository of research (including dissertations and scholarly articles) by members of the Harvard community.
Doctoral Dissertations in Musicology - Online (DDM) A bibliography of completed dissertations and proposed topics in musicology, music theory, ethnomusicology, and related disciplines. Maintained by the American Musicological Society.
MTO Dissertation Index An index of dissertations in music theory, with abstracts and tables of contents, maintained by the Society for Music Theory.
<< Previous: Collected Works Editions
Next: Music in Special Collections >>

Except where otherwise noted, this work is subject to a Creative Commons Attribution 4.0 International License , which allows anyone to share and adapt our material as long as proper attribution is given. For details and exceptions, see the Harvard Library Copyright Policy ©2021 Presidents and Fellows of Harvard College.

University Library

Dissertation and Thesis Research and Writing Guide for Music Students

Getting started.

Conducting Your Research
Writing Your Thesis or Dissertation
Citation Resources
Copyright Permissions
Depositing Your Thesis or Dissertation

Music & Performing Arts Library

This is a guide to library resources for graduate students in the School of Music working on a thesis or dissertation.

Use the tabs to the left to navigate the guide and see what resources we have available.

If you have questions about the Library or accessing resources related to your thesis or dissertation work that we didn't cover in this guide, please let us know! You can contact the librarians at [email protected] or by clicking the "Email Me" button on the left.

***Please note: the information included in this guide regarding graduation and dissertation requirements is intended as a guideline only. Always check with the School of Music or the Graduate College Thesis Office if you have questions about these requirements as they will be best able to provide up to date information.

Getting Help

Need research help.

If you need help with your research or are having trouble tracking down the sources you need, you can make an appointment with a librarian to discuss your research one-on-one. Don't hesitate to reach out or schedule an appointment if you need help! You are also always welcome to ask for assistance at the service desk at MPAL.

Questions About Graduate Requirements?

If you have questions about graduate requirements, you can reach out to the School of Music Graduate Academic Affairs for clarification. They can be reached via email at [email protected]

We are also including links to the Graduate College Handbook and the Thesis Office below in case you want to consult policies or requirements yourself.

Graduate College Handbook of Policy and Requirements
Thesis Office
School of Music's Student Resources

Guides to Researching & Writing About Music

Research Guides
Writing Guides

As you conduct your research, you may find it helpful to consult some of the handbooks below to help guide you through the research process. For help with writing about music - including selecting the right terminology as well as general style tips - be sure to check out the next tab of this box!

Next: Conducting Your Research >>
Last Updated: Jun 5, 2024 3:41 PM
URL: https://guides.library.illinois.edu/musicdissthesis

Music Citation Guide (Chicago Style)

Music librarian.

What kind of examples are included on this page?

As you scroll, you'll find footnote and bibliography entry templates and examples of citations for real sources for the following types of dissertations and theses:

Digital dissertations and theses
Print dissertations and theses

Digital dissertation or thesis

Footnote template:

8. Author First Name Last Name, “Dissertation or Thesis Title” (DMA diss./PhD diss./master’s thesis, Institution Name, Year), Page Number, Database Name.

Example of a real footnote:

8. Christine Jobson, “Florence Price: An Analysis of Select Art Songs With Text by Female Poets” (DMA diss., University of Miami, 2019), 56, ProQuest Dissertations & Theses.

Bibliography entry template:

Example of a real bibliography entry:

Print dissertation or thesis

19. Author First Name Last Name, “Dissertation or Thesis Title’” (DMA diss./PhD diss./master’s thesis, Institution Name, Year), Page Number.

19. Gregory Alden Magie, “Conducting William Schuman's ‘New England Triptych’’” (DMA diss., University of California, Los Angeles, 1996), 12.

<< Previous: Books
Next: Encyclopedias and dictionaries (including Grove/Oxford Music Online, Garland, etc.) >>

Bodleian Libraries
Oxford LibGuides
Dissertations

Music: Dissertations

Online Resources
Collections overview

Dissertations on music

Oxford dissertations have traditionally been deposited in hard-copy in the Bodleian and are treated as manuscripts. All doctoral dissertations now have entries on SOLO and many more recent theses are available in full-text through Oxford's research repository ORA . Composition degrees also involve the deposit of portfolio submissions or degree 'exercises', many of which (some dating back to the 18th century) are held in the Weston Library.

For general information on accessing theses and dissertations, click here .

The principal published lists of dissertations on music are:

Doctoral Dissertations in Musicology – Online . DDM is a free international database of bibliographic records for completed dissertations and new dissertation topics in the fields of musicology, music theory, and ethnomusicology, as well as in related musical, scientific, and humanistic disciplines. Hosted by the American Musicological Society, the database currently containing over 14,000 records, including the corrected and updated contents of all earlier printed editions of Doctoral Dissertations in Musicology and its supplements contributed from musicological centres throughout the world.
ProQuest Dissertations & Theses Global (PQDT) is the world's most comprehensive collection of full-text dissertations and theses in all subjects. As the official digital dissertations archive for the Library of Congress and as the database of record for graduate research, PQDTGlobal includes millions of searchable citations to dissertations and theses from 1861 to the present day together with over a million full-text dissertations that are available for download in PDF format. Over 2.1 million titles are available for purchase as printed copies. The database offers full text for most of the dissertations added since 1997 and strong retrospective full-text coverage for older graduate works. It also includes content from PQDT UK & Ireland (aka Index to Theses).
EThOS (Electronic Theses Online Service) is a British Library service covering dissertations from most British universities. Search over 500,000 doctoral theses and register for free to download instantly for your research, or order a scanned copy quickly and easily. Hard-copy theses can sometimes be made available in the Bodleian on Inter-Library Loan .
Archive of Dissertation Abstracts in Music provides abstracts both to completed dissertations and to those in progress.

Other sources for European dissertations:

DART-Europe E-theses Portal provides access to 1,079,176 open access research theses from 570 Universities in 29 European countries.
Full retrospective lists of German-language dissertations to 1970 will be found in Richard Schaal: Verzeichnis deutschsprachiger musikwissenschaftlicher Dissertationen (Bärenreiter, 1963; with supplement 1974). Retrospective coverage of French dissertations will be found in Jean Gribenski's printed guide: Thèses de doctorat en langue française relatives à la musique (Pendragon Press, 1979).
Many printed dissertations from European universities, and a small number of American dissertations, are held by the Bodleian. Most can be found on SOLO .
<< Previous: Online Resources
Next: Collections overview >>
Last Updated: Jun 26, 2024 5:21 PM
URL: https://libguides.bodleian.ox.ac.uk/music

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The Philosophy of Music

Philosophy of music is the study of fundamental questions about the nature and value of music and our experience of it. Like any “philosophy of X,” it presupposes knowledge of its target. However, unlike philosophy of science, say, the target of philosophy of music is a practice most people have a significant background in, merely as a result of being members of a musical culture. Many people take music to play a significant and valuable role in their lives. Thus, as with the central questions of metaphysics, epistemology, and ethics, not only can most people quickly grasp the philosophical questions music raises, they tend to have thought about some of those questions before encountering the academic discipline itself.

Music arguably presents more philosophical puzzles than any other art. Unlike painting, its works often have multiple instances, none of which can be identified with the work itself. Thus, the question of what exactly the work is is initially more puzzling than the same question about works of painting, which appear (at least initially) to be ordinary physical objects. Unlike much literature, the instances of a work are performances, which offer interpretations of the work, yet the work can also be interpreted (perhaps in a different sense) independent of any performance, and performances themselves can be interpreted. This talk of “interpretation” points to the fact that we find music an art steeped with meaning, and yet, unlike drama, music—at least “pure” instrumental music—has no obvious semantic content. This quickly raises the question of why we should find music so valuable. Central to many philosophers’ thinking on these subjects has been music’s apparent ability to express emotions while remaining an abstract art in some sense.

This entry focuses almost exclusively on contemporary philosophy of music (i.e., work since the mid-twentieth century) in an analytic vein. For a historical overview, see the entries on the history of Western philosophy of music: antiquity to 1800 and history of Western philosophy of music: since 1800 . For much broader introductions to philosophy of music, covering its history, major figures, connections with other disciplines, and a wider range of topics, see Gracyk & Kania 2011 and McAuley, Nielsen, & Levinson 2021. Useful single-author overviews include Scruton 1997, Kivy 2002, Hamilton 2007, and Kania 2020.

Most analytic work has primarily discussed Western classical music. (For criticism of this tendency, see Alperson 2009.) In the last 25 years, there has been increasing recognition that different musical practices may not only suggest different answers to the same philosophical questions, but also raise different philosophical questions. Apart from Western classical music, popular Western traditions, such as rock and jazz, have received the most attention. Non-Western musical traditions have received little attention. (Exceptions include S. Davies 2001: 254–94 and 2007; Alperson, Nguyen, & To 2007; S. P. Walton 2007; and Higgins 2007.)

1.1 Music Alone and Together

1.2 the definition of “music”, 2.1 the fundamentalist debate, 2.2 higher-level ontological issues, 3.1 emotions in the music, 3.2 emotions in the listener, 4.1 basic musical understanding, 4.2 higher-level musical understanding, 5.1 music’s artistic value, 5.2 music’s moral value, other internet resources, related entries, 1. what is music.

It is plausible that song is the most common kind of music listened to across history and the globe. Moving images (film, television, videogames, etc.) are ubiquitous in the contemporary world, and most have musical soundtracks. Nonetheless, most philosophy of music considers what Peter Kivy calls music alone (1990)—instrumental music with no non-musical aspects, elements, or accompaniments. At least three reasons can be given to defend this narrow focus. First, pure music often presents the most difficult philosophical problems. The maudlin text of a song plausibly contributes to the song’s expressiveness; it is more puzzling how a piece of music alone could be emotionally expressive. Second, though the problems are more difficult, the solutions are likely to be more easily evaluated with respect to music alone. Just as apportioning blame is easier when one person is responsible for a crime than when the blame must be divided between a number of conspirators, the success of a solution to the problem of musical expressiveness may be clearer if it can explain the expressiveness of music alone. Third, the expressiveness of music alone will play a role in the expressiveness of musical hybrids such as song or film. Though its text may contribute to the expressiveness of a song, for instance, the musical aspects of the song must play some role. A maudlin text set to a jauntily upbeat melody will clearly not have the same overall expressiveness as the same text set to a plodding dirge. Though expressiveness is used as an example here, these same points apply to discussions of musical understanding and value.

Even if these three reasons are compelling (see Ridley 2004 for a sustained critique), music’s combination with other media raises further philosophical questions. There is no space to consider those questions here, but on the aesthetics of song, see Levinson 1987; Gracyk 2001; Bicknell & Fisher 2013; and Bicknell 2015. On music drama, see Kivy 1988b, 1994; Goehr 1998; and Penner 2020. On film music, see Carroll 1988: 213–225; Smith 1996; Levinson 1996b; and Kivy 1997a. See also the chapters in part V of Gracyk & Kania 2011. On hybrid art forms more generally, see Levinson 1984 and Ridley 2004.

Explications of the concept of music usually begin with the idea that music is organized sound. They go on to note that this characterization is too broad, since there are many examples of organized sound that are not music, such as human speech, or the sounds non-human animals and machines make. There are two further kinds of necessary conditions philosophers have added in attempts to fine tune the initial idea. One is an appeal to “tonality” or essentially musical features such as pitch and rhythm (Scruton 1997: 1–79; Hamilton 2007: 40–65; Kania 2011a). Another is an appeal to aesthetic properties or experience (Levinson 1990a; Scruton 1997: 1–96; Hamilton 2007: 40–65). As these references suggest, one can endorse either of these conditions in isolation, or both together.

The main problem with the first kind of condition is that every sound seems capable of being included in a musical performance, and thus characterizing the essentially musical features of sounds seems hopeless. (We need only consider the variety of “untuned” percussion available to a conservative symphonist, though we could also consider examples of wind machines, typewriters, and toilets, in Ralph Vaughan Williams’s Sinfonia Antartica , Leroy Anderson’s The Typewriter , and Yoko Ono’s “Toilet Piece/Unknown.”) Defenders of such a condition have turned to sophisticated intentional or response-dependent theories of tonality in order to overcome this problem. If the essentially musical features of a sound are not intrinsic to it, but somehow related to how it is produced or perceived, we can classify just one of two “indiscernible” sounds as music.

If one endorses only an aesthetic condition, and not a tonality condition, one still faces the problem of poetry—non-musical aesthetically organized sounds. Levinson, who takes this approach, excludes organized linguistic sounds explicitly (1990a: 272). This raises the question of whether there are further distinctions to be made between arts of sound. Andy Hamilton defends a tripartite division, arguing that sound art , as opposed to both music and literature, was established as a significant art form in the twentieth century (2007: 40–65). This is one reason that Hamilton endorses both tonal and aesthetic conditions on music; without the former, Levinson is unable to make such a distinction. On the other hand, by endorsing an aesthetic condition, Hamilton is forced to exclude scales and Muzak, for instance, from the realm of music. Kania (2020: 296–301) suggests that it is a mistake to think that music is necessarily an art. He argues that we should distinguish the medium of music from its artistic uses, just as we do in the cases of language and literature, depiction and painting, and so on.

Jonathan McKeown-Green (2014) makes trenchant criticisms of definitions of music that assume that the nature of music is settled by our conception of music (395, italics removed). He argues that no such definition could be future-proof, since it would be hostage to our changing conception of music. At best, we would end up with a kind of sociological history of music that would fail to fulfill any of the functions of a definition. McKeown-Green singles out the definitions of Kania (2011a) and Levinson (1990a), stated in terms of necessary and sufficient conditions, as of this hopeless kind. But Kania (2020: 302–5) argues that McKeown-Green’s criticisms apply equally to the looser definitions of Hamilton and S. Davies (2012).

Having discussed complications, it’s worth returning to the basic idea of “organized sound.” Most theorists note that music does not consist entirely of sounds. Most obviously, much music includes rests. You might think that silence can function only to organize the sounds of music. One counterargument is that an understanding listener listens to the rests, just as she listens to the sounds (Kania 2010). Another is to provide putative cases of music in which the silences do not structure sounds as ordinary rests do. John Cage’s 4′33″ is frequently discussed, though there is broad agreement that this piece is not silent—its content is the ambient sounds that occur during its performance. (See Dodd 2018 for dissent.) Anyway, S. Davies (1997a), Dodd (2018), and Kania (2010) all argue that Cage’s piece is not music—Davies and Dodd because its sounds (if any) fail to qualify as organized, Kania because they fail to meet a tonality condition. Wadle (forthcoming) argues that the piece is music, because of its contextual connections to previous musical works. Kania considers several other contenders for the label of “silent music,” arguing that there are indeed extant examples, most notably Erwin Schulhoff’s “In Futurum” from his Fünf Pittoresken , which predates Cage’s 4′33″ by some 33 years.

2. Musical Ontology

Musical ontology is the study of the kinds of musical things there are and the relations that hold between them. The most discussed issues within this field have been the metaphysical nature of works of Western classical music (the “fundamentalist debate”), and what it is to give an “authentic performance” of such works. Recently there has been growing interest in the ontologies of other Western musical traditions, such as rock and jazz, and discussion of the methodology and value of musical ontology. (For more detailed overviews of these debates, see Matheson & Caplan 2011, and Nussbaum 2021.)

Musical works in the Western classical tradition admit of multiple instances (performances). Much of the debate over the nature of such works thus reads like a recapitulation of the debate over the “problem of universals”; the range of proposed candidates covers the spectrum of fundamental ontological theories. We might divide musical ontologists into the realists, who posit the existence of musical works, and the anti-realists, who deny their existence. Realism has been more popular than anti-realism, but there have been many conflicting realist views. We begin with two unorthodox realist views before moving on to more orthodox Platonist and nominalist theories, concluding with a consideration of anti-realism.

Idealists hold that musical works are mental entities. Collingwood (1938) and Sartre (1940) respectively take musical (and other) works to be imaginary objects and experiences. The most serious objections to this kind of view are that (i) it fails to make works intersubjectively accessible, since the number of works going under the name The Rite of Spring will be as multifarious as the imaginative experiences people have at performances with that name, and (ii) it makes the medium of the work irrelevant to an understanding of it. One might have the same imaginative experience in response to both a live performance and a recording of The Rite of Spring , yet it seems an open question whether the two media are aesthetically equivalent. But see Cox 1986 and Cray & Matheson 2017 for attempts to revive idealism.

David Davies argues that musical works, like all works of art, are actions , in particular the compositional actions of their composers (2004). Thus he revives what we might call an “action theory” of the ontology of art. (An earlier defender of such a view is Gregory Currie (1989), who argues that artworks are types of action, rather than the particular actions with which Davies identifies them.) Although deciding between theories of musical ontology is always to some extent a matter of finding a balance between the benefits of a theory and its cost in terms of our pre-theoretic intuitions, action theories have a particularly hard row to hoe since they imply that an instance of a work is some action performed by a composer, rather than a performance. In order to make up for such damage to our intuitions the theoretical benefits of an action theory would have to be quite extensive.

Most theorists think that some kind of Platonist or nominalist theory of musical works is more plausible than those so far considered. Platonism, the view that musical works are abstract objects, is arguably still the dominant view, though it seems to be losing ground to sophisticated nominalisms. Its great advantage is its ability to respect more of our pre-theoretic intuitions about musical works than other theories can. On the other hand, it is the most ontologically puzzling, since abstract objects are not well understood. Nonetheless, Platonism has been tenacious, with much of the debate centering around what variety of abstract object musical works are. What we might call “simple Platonism” (known simply as “Platonism” in the literature), is the view that works are eternal existents, existing in neither space nor time (Kivy 1983a, 1983b, Dodd 2007). Puy (2019) presents a variation according to which musical works are higher-order types, of which the types other Platonist thinks are works are specific versions of works. (See D. Davies 2021 for discussion.)

According to “complex Platonism,” musical works come to exist in time as the result of human action. The complexity is motivated by a number of features of musical practice, including the intuition that musical works are creatable, the attribution of various aesthetic and artistic properties to works, and the fine-grained individuation of works and performances (e.g., in terms of who composed them, or what instruments they are properly performed upon) (Ingarden 1961; Thomasson 2004; Wolterstorff 1980; Wollheim 1968: 1–10, 74–84; Levinson 1980, 1990b, 2012; S. Davies 2001: 37–43; Howell 2002; Stecker 2003a: 84–92).

Nominalists identify a musical work with something concrete. The most obvious candidate is a collection of performances, whether the collection be understood as a set (Goodman 1968; Predelli 1995, 1999a, 1999b, 2001), a fusion (Caplan & Matheson 2004, 2006), or something more esoteric (Tillman 2011; see also Moruzzi 2018). Charles Nussbaum (2007: 143–87) and P. D. Magnus (2012) argue for a close analogy between musical works and species. Nussbaum (2021: 334) points out that a sophisticated nominalist theory of species has been developed in great detail over the years by Ruth Millikan (1984, 2000). While such views are attractive because they appeal only to the least problematic kinds of entities, they face serious challenges. Though many of our claims about musical works may be paraphraseable into claims about sets of (possible) performances, some seem to make intractable reference to works. For instance, most performances of The Rite of Spring—even including the possible ones—include several wrong notes. Thus it is difficult to imagine how the paraphrase schema will avoid the nonsensical conclusion that The Rite of Spring contains several wrong notes, if the work consists entirely of performances. In response to this problem, most nominalists add to the collection of performances some provenential item, such as an original score or act of composition. Whether this addition can solve the problem without necessitating the reintroduction of an abstract entity is one question any nominalist must address.

Intermediate between Platonism and nominalism are the views of Philip Letts (2018) and Guy Rohrbaugh (2003). Letts argues that any view of musical works as types would be improved by identifying those types with their associated properties, a proposal that may be developed in a Platonist or nominalist direction. Rohrbaugh’s view of musical works as historical individuals “embodied in,” but not constituted by, physical things such as scores and performances closely resembles to the views of Nussbaum and Magnus, discussed above, but Rohrbaugh takes the work to be an abstract object over and above its embodiments. (For discussion, see Dodd 2007: 143–66.)

In contrast to all these realist views stand those of the anti-realists, who deny that there are any such things as musical works. An early proponent of such a view is Richard Rudner (1950), though it is difficult to say whether he is best interpreted as an eliminativist or a fictionalist, the two anti-realist views currently on the table. According to eliminativists, there are no such things as musical works, and thus we ought to stop trying to refer to them. Ross Cameron (2008) defends such a view, but only with respect to “Ontologese”—the language we speak when we do ontology. He argues that ordinary English locutions such as “there are many musical works” can be true without there being any musical works. (For critical discussion, see Predelli 2009 and Stecker 2009.) According to fictionalists, the value of discourse about musical works is not truth, and thus we ought not to abandon the discourse despite the non-existence of its subject matter, but rather to adopt a different, make-believe attitude towards it (or perhaps we already do so). (See Kania 2008; for criticism, see D. Davies 2011: 45–50, Letts 2015, and Nussbaum 2021: 337.)

Much of this debate over the fundamental ontological category to which musical works belong has turned on “technical” issues, that is, controversial general metaphysical claims about the nature of properties, causation, embodiment, and so on (e.g., Howell 2002; Trivedi 2002; Caplan & Matheson 2004, 2006; Dodd 2007; Hazlett 2012; Kleinschmidt & Ross 2012; Dodd & Letts 2017; Cameron 2008). In the face of this, some theorists have pointed out that musical works are cultural entities, and thus the methodology appropriate to uncovering their ontological status might be quite different from that of general metaphysics (Goehr 1992; S. Davies 2003a; D. Davies 2004; Thomasson 2006). For further discussion of the methodology of musical ontology, see D. Davies 2009, 2017; Predelli 2009; Stecker 2009; Dodd 2010, 2013; Mag Uidhir 2012b; and Nussbaum 2021.

It might seem that, since musical works are ontologically multiple, once we have figured out their true nature, we will know what relation holds between the work and its performances, namely, whatever relationship holds between entities of that kind and their instances. However, since the fundamentalist debate is about the basic ontological category to which works belong, resolving that debate may leave open many questions about the relation between a work and its performances. For instance, is the use of a harpsichord required to instance Bach’s Brandenburg Concerto No. 5 in performance? Would producing harpsichord-like sounds on a synthesizer do just as well? What about using another keyboard instrument from Bach’s time, or a modern piano? Learning that musical works are, say, eternal types will not necessarily help settle this issue of “authentic performance,” which is perhaps the most discussed music-ontological issue, of interest to philosophers, musicologists, musicians, and audiences alike. (For an excellent overview of the authentic performance debate, see S. Davies 2001: 201–53. For an investigation of authenticity with respect to things other than instantiation of the work, see Kivy 1995; Gracyk 2001, 2009, 2017; Bicknell 2015; and Cray 2019.)

There have been two sources of widespread confusion in the debate over authenticity in performance. One is a failure to recognize that authenticity is not simply a property, but a relation that comes in degrees and holds with respect to different aspects of its target. Something may be more authentic in one regard and less authentic in another (S. Davies 2001: 203–5). Another is the assumption that authenticity is an evaluative concept, in the sense that “authentic” implies “good.” That this is not the case is clear from the fact that an authentic murderer is not a good thing (S. Davies 2001: 204). Thus, our value judgments will be complex functions of the extent to which we judge performances authentic in various regards, and the values we assign to those various kinds of authenticity.

The central kind of authenticity that has been discussed is authenticity with respect to the instantiation of the work. Most agree that the fullest such authenticity requires the production of the right pitches and rhythms in the right order. (For skepticism based on the history of the practice, see Dyck 2014; Ravasio 2019a; and the discussion in Dodd 2020b and Ravasio 2020.) Pure sonicists argue that this is sufficient (e.g., Kivy 1988a). Timbral sonicists argue that these pitches must also have timbres reflecting the composer’s instrumentation (e.g., Dodd 2007: 201–39). Instrumentalists argue that such sounds must be produced on the kinds of instruments specified in the score (e.g., Levinson 1990c). Much of the debate is over what kinds of aesthetic or artistic properties are essential to musical works. If the limpid textures of Bach’s Brandenburg Concerto No. 5 are essential to it, then one cannot authentically instance the work using a grand piano instead of a harpsichord. As such, the debate reflects a wider one in aesthetics, musical and otherwise, between formalists (or empiricists, or structuralists), who believe that the most important properties of a work are intrinsic ones, accessible to listeners unaware of the historical and artistic context in which it was created, and contextualists , who believe that a work is essentially tied to its context of creation. Stephen Davies has argued for a strong contextualism, claiming that one cannot give a single answer to the question of whether particular instrumentation is required for the fully authentic instantiation of a work. Works can be ontologically “thicker” or “thinner” as a result of the specifications of a composer working within certain conventions (1991, 2001). The more properties of a fully authentic performance a particular work specifies, the thicker it is. Thus for some works (typically earlier in the history of Western music) instrumentation is flexible, while for others (e.g., Romantic symphonies) quite specific instrumentation is required for fully authentic performances.

In addition to the question of what constitutes authenticity, there has been debate over its attainability and value. Those who question its attainability point to our historical distance from the creation of some works (Young 1988). We may no longer be able to read the notation in which the work is recorded, or construct or play the instruments for which it was written. If so, full authenticity is not attainable. But we rarely have no idea about these matters, and thus we might achieve partial authenticity (S. Davies 2001: 228–34). Those who question the value of authenticity often target kinds other than work-instantiation. For instance, one might question the value of producing a performance that authentically captures the sound of performances as they took place in the context of a work’s composition, on the basis that musicians were not as highly skilled then as now, for instance (Young 1988: 229–31). Such arguments, though, have no consequences for the value of work-instantiation. Some argue that although we might attain an authentic instance of a work, the idea that we might thereby hear the work as its contemporaries heard it is wishful thinking, since the musical culture in which we are immersed enforces ways of listening upon us that we cannot escape (Young 1988: 232–7). Thus the point of such authenticity is questioned. In response, we may consider not only the possibility that we are in a better position to appreciate historical works than contemporary ones, but also the remarkable flexibility people seem to show in enjoying many different kinds of music from throughout history and the world (S. Davies 2001: 234–7).

Julian Dodd (2020a) argues that there is more than one way to be true to a musical work, and thus to produce an authentic performance: One can comply with the score, or one can be true to the music’s overall integrity or point (136). When the two conflict, interpretive authenticity trumps score-compliance authenticity (147) because the fundamental norm of work-performance practice is to perform it in a way that evinces a subtle or profound understanding of it (163), while score compliance is valued only because it tends to lead to such performances. Andrew Kania responds that it is unclear whether, even by the lights of Dodd’s own theory, Dodd’s central examples are cases of interpretive authenticity trumping score compliance (Kania 2022: 131–2). More importantly, he argues that Dodd’s conception of the music’s overall integrity or point misses the importance of the surface-level details to a work’s meaning or content. Kania suggests, instead, that the fundamental norm of the practice is to evince an understanding of the work through complying with its score (2022: 127, italics altered).

Moving on from authenticity, a second area that may be independent of the fundamentalist debate is that of comparative ontology. (For dispute over this framing issue, see Brown 2011, 2012.) Just as classical works from different historical periods may be ontologically diverse, so may works from different contemporary traditions. Theodore Gracyk has argued that instances of works of rock music are not performances. Rather, the work is instanced by playing a copy of a recording on an appropriate device (1996; cf. Fisher 1998). Stephen Davies has argued that rock is more like classical music than Gracyk acknowledges, with works for performance at the heart of the tradition, albeit works for a different kind of performance (2001: 30–6). Gracyk’s view has been amplified and defended in attempts to find a place for composition, live performance, and performance skill within his basic framework (Kania 2006, Bruno 2013, Bartel 2017, Magnus 2022).

Work on the ontology of jazz has centered on the nature of improvisation, particularly the relation between improvisation and composition (Alperson 1984, 1998; Valone 1985; Brown 1996, 2000; Hagberg 1998; Gould & Keaton 2000; Sterritt 2000; and Young & Matheson 2000; Bresnahan 2015; Love 2016; Magnus 2016). This has been a useful reminder that not all music is the performance of pre-composed works (Wolterstorff 1987: 115–29). However, improvisation can occur within the context of such a work, as in the performance of an improvised cadenza in a classical concerto. Some have argued that there is not as significant a distinction between improvisation and composition as is usually thought (Alperson 1984). Others have argued that all performance requires improvisation (Gould & Keaton 2000). Yet others restrict the possibility of improvisation to certain kinds of musical properties, such as “structural” rather than “expressive” ones (Young & Matheson 2000). However, none of these arguments are compelling. Usually they turn on equivocal use of terms such as “composition” and “performance,” or beg the question by defining improvisation in terms of deviation from a score or variation of a limited set of “expressive” properties.

Though jazz is not necessarily improvisational, and very few jazz performances lack any sort of prior compositional process, the centrality of improvisation to jazz presents a challenge to the musical ontologist. One might argue that jazz works are ontologically like classical works—composed for multiple, different performances—but that they tend to be thinner, leaving more room for improvisation (Gould & Keaton 2000; Young & Matheson 2000). The difficulty is to specify the work without conflating one work with another, since tokening the melody may not be required, and many works share the same harmonic structure. As a result, some argue that the performance is itself the work (Alperson 1984; Hagberg 2002; S. Davies 2001: 16–19). One problem here is parity with classical music. If jazz performances are musical works in their own right, it is difficult to deny that status to classical performances of works, yet this seems to multiply works beyond what we usually think is necessary. A third possibility is that in jazz there are no works, only performances (Brown 1996, 2000: 115; Kania 2011b). This is counterintuitive if “work” is an evaluative term, but it is not obvious that this is the case.

Julian Dodd (2014a) argues that the kinds of considerations adduced in favor of these views confuse questions of ontology with questions of value. Jazz is ontologically like early classical music, according to Dodd: the focus of critical attention is the improvisatory performance rather than the composition it instantiates, but that composition is no less a musical work for that difference in critical emphasis. (See Fisher 2018 for an attempted reconciliation.) Similar considerations might be adduced against the increasingly complicated ontologies of rock referred to above. Such arguments return us to debates about the methodology of musical ontology.

3. Music and the Emotions

The most widely discussed philosophical question concerning music and the emotions is that of how music can express emotions. (For a more extensive introduction, see part II of Gracyk & Kania 2011; for a thorough treatment, see S. Davies 1994.) There is a second group of questions centered around listeners’ emotional responses to music. These include questions about why and how we respond emotionally to music, the value of such responses, and why we choose to listen to music that elicits “negative” responses from us, such as sadness. Theorists typically restrict themselves to “pure” or “absolute” music on the grounds that it is easier to understand how music with an accompanying text, say, could express the emotions evident in the text. However, an important criterion for the evaluation of such music is how appropriately the composer has set her chosen text to music. So an accompanying text is clearly not sufficient for the musical expression of an emotion. Thus, a better reason for initially putting such music to one side is perhaps that the interrelation of music and text, or other elements, is likely to be highly complex, and best approached with as well-developed a theory of the more basic phenomena in hand as possible. (For an extended criticism of this approach, see Ridley 2004: 1–104.)

Pieces of music, and performances of them, are standardly said to be happy, sad, and so on. Music’s emotional expressiveness is a philosophical problem since the paradigm expressers of emotions are psychological agents, who have emotions to express. Neither pieces of music, nor performances of them, are psychological agents, thus it is puzzling that such things could be said to express emotions.

One radical way to solve the puzzle is to deny that music is emotionally expressive. A major burden of such eliminativism is to explain away the widespread tendency to describe music in emotional terms. This has been attempted by arguing that such descriptions are shorthand or metaphor for purely sonic features (Urmson 1973), basic dynamic features (Hanslick 1854), purely musical features (Sharpe 1982), or aesthetic properties (Zangwill 2007). There are many problems with such views. For one thing, they seem committed to some sort of scheme for reduction of expressive predicates to other terms, such as sonic or musical ones, and such a scheme is difficult to imagine (Budd 1985a: 31–6). For another, anyone not drawn to this theory is likely to reject the claim that the paraphrase captures all that is of interest and value about the passage described, precisely because it omits the expressive predicates (Davies 1994: 153–4).

Conventionalism is the view that music’s expressiveness is a matter of the conventional association of certain musical elements, such as slow tempi, with certain emotional states, such as sadness. Such conventions must play a role in some cases of expression—for instance, cases of particular musical instruments (e.g., the snare drum) being associated with particular situations (e.g., war) and thus emotions (e.g., foreboding). But such conventions seem unlikely to account for all musical expressiveness, since much of that expressiveness seems less arbitrary than conventionalism would suggest. It seems implausible, for instance, that the convention for funeral dirges might just as easily have that they should be quick-paced and in major keys. Even in cases like the snare drum, it seems possible that the instrument was chosen for the battlefield in part because of the expressive character of its sonic profile.

The cliché that music is “the language of the emotions” is often considered as a possible starting point for a theory of musical expressiveness. The idea combines the attractive simplicity of conventionalism with the formalist notion that music’s order is to be understood in terms of syntax. (See Lerdahl & Jackendoff 1983 for a theory along the latter lines.) However, although Deryck Cooke (1959) and Leonard Meyer (1956) are often cited as proponents, it is not clear that anyone holds a full-blown version of the theory. The central problem is the great disparities between language and music, in terms of the ways in which each is both syntactic and semantic (Jackendoff 2011). A serious subsidiary problem is that even if music were about the emotions in the way that language can be, that would not account for music’s expressiveness . The sentence “I am sad” is about the emotions, but it is not expressive of sadness in the way a sad face is, though you could use either to express your sadness. Most people agree that music’s relation to emotion is more like that of a sad face than that of a sentence. (This last criticism is also applicable to Susanne Langer’s theory (1953) that music is about the emotions in a symbolic yet non-linguistic way.)

We now turn to theories that attempt to connect the notion of music’s expressiveness to actual felt emotions. One obvious way to do so is to argue that pieces of music or performances of them are expressions of such emotions—those of the composer or performer. There are two major problems with this “expression theory.” The first is that neither composers nor performers often experience the emotions their music is expressive of as it is produced. Nor does it seem unlikely that a composer could create, or a performer perform, a piece expressive of an emotion that she had never experienced. This is not to deny that a composer could write a piece expressive of her emotional state, but for the expression theory to be an account of musical expressiveness, at least all central cases of expressiveness must follow this model, which is not the case. Moreover, if a composer is to express her sadness, say, by writing a sad piece, she must pen the right kind of piece. In other words, if she is a bad composer she might fail to express her emotion. This brings us to the second major problem for the expression theory. If a composer can fail to express her emotions in a piece, then the music she writes is expressive independent of the emotion she is experiencing. Thus music’s expressiveness cannot be explained in terms of direct expression.

Those usually cited as classic expression theorists include Tolstoy (1898), Dewey (1934), and Collingwood (1938). (A classic critique is Tormey 1971: 97–127.) These theorists have been defended in recent discussions, however, from accusations that they hold the simple view outlined above (Ridley 2003, Robinson 2005: 229–57). Jenefer Robinson has attempted to revive the expression theory, though she defends it as an interesting and valuable use of music’s expressiveness, rather than an account of expressiveness itself (2005: 229–347; 2011).

A second way to link music’s expressiveness with actual felt emotions is through the audience. According to arousalism, the expressiveness of a passage of music amounts to its tendency to arouse that emotion in a competent listener. Arousalism faces several objections. First, some competent listeners seem emotionally unmoved by music (or are at least not moved to the specific emotions expressed by it). But perhaps the arousalist can simply restrict the class of listener to which his theory appeals to those who are so moved. Second, some emotions, such as fear, require a particular kind of intentional object (something threatening), yet there is no such object at hand when we hear fearful music. Thus it seems implausible to claim the music’s fearfulness resides in its arousal of fear in us. But perhaps the arousalist can broaden the class of aroused emotions to include appropriate responses to the expressed emotion, such as pity. Third, in many cases it seems that listeners respond emotionally to the expressiveness of the music. It is not clear that the arousalist can handle such cases non-circularly. (A sophisticated defense of the arousal theory is to be found in Matravers 1998: 145–224, though see the second thoughts in Matravers 2011. For an extended critique, see S. Davies 1994: 104–200.)

Despite the problems of the arousal theory as the whole story of musical expressiveness, there is a growing consensus, thanks largely to the work of Jenefer Robinson (1994, 2005), that our lower-level, less cognitive responses to music must play some role in the emotional expressiveness we attribute to it. However, this role is likely to be a causal one, rather than part of an analysis of what it is for music to be emotionally expressive.

Several theorists have defended accounts of musical expressiveness known variously as resemblance, contour, or appearance theories (e.g., Kivy 1989, though see Kivy 2002: 31–48 for recent qualms; Budd 1995: 133–54; S. Davies 1994: 221–67). The central idea is that music’s expressiveness consists in the resemblance between its dynamic character and that of various typical aspects of people experiencing emotions. The aspects appealed to include the phenomenology of the experience of the emotion, the emotion’s facial expression, the contour of vocal expression or bodily behavior of a person experiencing the emotion. Stephen Davies argues that such theories hold music to be expressive in a literal albeit secondary sense of the term. We say that a piece of music is sad in the same sense in which we say that a weeping willow is sad (S. Davies 2006: 183). Such uses are no more metaphorical than a claim that a chair has arms.

Jerrold Levinson agrees that there is an important resemblance between the contour of music expressive of an emotion and the contour of typical behavioral expressions of that emotion. He objects, however, that such an account cannot be the whole, or even the most fundamental part of the story (Levinson 1996a, 2006b). He drives in a wedge precisely at the point where an appeal is made to the resemblance between the music and typical behavioral expressions. He asks what the manner and extent of the resemblance between the two must be, precisely, in order for the music to count as expressive of some emotion. After all, everything resembles everything else in all sorts of ways, and so one could point out many resemblances between a funeral march and an expression of joy, or for that matter a cup of coffee and sadness. The resemblance theorist must give some account of why the funeral march, and not the cup of coffee, is expressive of sadness and not joy. Levinson claims that the obvious answer here is that the funeral march is “readily hearable as” an expression of sadness. If this is correct, then the resemblance the music bears to emotional behavior is logically secondary—a cause or ground of its expressiveness. The expressiveness itself resides in the music’s disposition to elicit the imaginative response in us of hearing the music as a literal expression of emotion. As a logical consequence, the imaginative experience prompted must include some agent whose expression the music literally is.

In reply to this kind of objection, Stephen Davies has emphasized the role of the listener’s response in resemblance theories. Such responses have always been appealed to by such theories, as evidenced by Malcolm Budd’s talk of “hearing as” (1995: 135–7), and Peter Kivy’s discussion of our tendency to “animate” that which we perceive (1980: 57–9). But Davies now makes the appeal quite explicit and central, devoting as much space to explication of the response-dependent nature of expressiveness as to the role of resemblance (2006). For Davies, the response of the competent listener upon which the expressiveness of the music depends is one of an experience of resemblance rather than imagined expression (2006: 181–2). Matteo Ravasio (2019b) argues that this leads to further problems.

Since Davies’s theory posits at base a contour-recognition experience while Levinson’s posits an imaginative experience of expression, the link between literal expression and musical expressiveness looks closer in Levinson’s theory than in Davies’s. An empirical consequence seems to be that Davies’s theory will predict weaker emotional responses to music than Levinson’s. Whether or not this is an advantage or disadvantage of the theory depends on the empirical facts about how we respond emotionally to music.

There are three main questions asked about our emotional responses to pure music, apart from what role they play in expressiveness. The first is analogous to the “paradox of fiction.” It is not clear why we should respond emotionally to music’s expressiveness when we know that no one is undergoing the emotions expressed. The second is a variant of the “paradox of tragedy.” If some music arouses “negative” emotional responses in us, such as sadness, why do we seek out the experience of such music? This leads to the more general question of the value of our emotional responses to music. The first two questions are addressed in this section, and the third in section 5.1.

Peter Kivy (1999) argues that those who report emotional reactions to music are confusing the pleasure they take in the beauty of the music, in all its expressive individuality, with the feeling of the emotion expressed. Though most philosophers appeal to ordinary experience and empirical data to reject the plausibility of Kivy’s position, they admit the problem that motivates it, namely, the conceptual tension between the nature of music and the nature of the emotions we feel in response to it. There is some consensus that emotions are cognitive, in the sense that they take intentional objects—they are about things—of certain kinds. For instance, in order to feel fear , one must believe that something is threatening (the “intentional object” of the emotion). When one listens to a sad piece of music, however, one knows there is nothing literally feeling sad, and thus it is puzzling that one should be made sad by the experience.

Part of the solution is that not all emotional responses (broadly construed) are cognitive (Robinson 1994; 2005: 387–400). For instance, it is no more puzzling that one could be startled by a fortissimo blow to a bass drum than that one could so respond to a thunderclap. Another part of the solution is that the music can be the object of our emotions, as when we are delighted by an effective ending to a long and complex piece.

As for emotional responses to music’s expressiveness, there are at least two possible explanations. One appeals to the phenomenon of “emotional contagion” or “mirroring responses” (S. Davies 1994: 279–307; 2006: 186–8). When surrounded by moping people, one tends to become sad. Moreover, such a “mood” is not about some intentional object. One is not necessarily sad for the mopers, nor whatever they are sad about, if anything. Similarly, when “surrounded” by music that presents an appearance of sadness, one might become sad, but not sad about the music, or anything else (Radford 1991). For critical discussion, see Robinson 2005: 379–412 and S. Davies 2011b.

If our experience of music’s expressiveness necessarily involves imagining that the music is a literal expression of emotion, then our emotional responses to that expressiveness are no more puzzling than emotional responses to other imagined expressive agents, such as fictional characters in novels. The advantage is only slight because the question of how and why we respond emotionally to fictions is itself a philosophical problem of some magnitude. Nonetheless, there are several theories available (see the supplement to the entry on imagination, §2 ). One difficulty with appealing to a solution to the paradox of fiction is that it is not clear that our emotional responses to the expressiveness of music are the same as those to emotionally expressive characters. For instance, the standard example of an emotional response to music is being made sad by a dirge, while the standard example of emotional response to fiction is (something like) to feel pity for a sad character. If the former is to be explained in the same way as the latter, we would expect listeners to feel pity in response to the funeral march (pity for the persona imagined to be expressing her sadness through it). However, we surely do feel sad (in some sense) in response to tragedy, and it is not obvious that we do not feel pity (or imagined pity, or whatever one’s preferred theory of emotional response to fiction posits) in response to sad music.

Leaving behind the topic of how and why we respond emotionally to music, we turn to the question of why we seek out music that arouses “negative” emotions in us, such as sadness, assuming henceforth that we are in fact aroused to such emotions. (Since this problem is a close analog of the “paradox of tragedy,” some of the references below are to literature not explicitly about music, but the transposition of the arguments to music is not difficult to imagine. (See also the supplement to the entry on imagination, §3 .) Most solutions assume that our negative emotional response is a price we are willing to pay for the benefits of engaging with the piece in question. The benefits appealed to include understanding and appreciating the music, including the expressiveness responsible for the negative response (Goodman 1968: 247–51; S. Davies 1994: 311–20; Goldman 1995: 68; Robinson 2005: 348–78).

A different benefit is Aristotelian catharsis , in which our negative emotional response to expressive art results in a psychological purgation of the negative emotions (Aristotle 1987: 6, 1449b21–1450b20). A less therapeutic approach is the suggestion that, since these emotions are without “life implications” (that is, as discussed above, we are not sad about any actual tragic events), we are able to take advantage of our responses to savor these emotions, gain an understanding of them, and be reassured that we have the capacity to feel them (Levinson 1982). Two things that must be explained by any defender of this kind of response are, first, our persistence in seeking out music that elicits negative emotional experiences after we have received the resulting benefit and, second, the enjoyment we seem to take in these negative responses, as opposed to putting up with them for their related benefits.

A different kind of solution to the problem argues that responses such as sadness that are evoked by expressive music are not really negative. Hume (1757) argues, with respect to tragedy, that the pleasure we take in the mode of presentation of the content of an artwork does not simply counterbalance the negative emotion evoked, but rather subsumes and transforms it into a pleasurable feeling. Kendall Walton argues (also with respect to tragedy) that sadness is not in itself negative. Rather, it is the situation to which sadness is the response that is negative. Thus, though we would not seek out the death of a loved one, given the death we “welcome” the sorrow (K. Walton 1990: 255–9). Similarly, we cannot affect the sadness of a musical work by not listening to it, and so we welcome our sorrowful response to it as appropriate. Berys Gaut (2007: 203–26) argues that though sadness is typically aroused by situations we would prefer to avoid, sadness in response to artistic expressiveness is an exception and thus not negative in any paradoxical way. A difficulty for all three solutions is the extent to which they accord with our emotional experience in rejecting the characterization of our sadness as negative.

4. Understanding Music

A central topic in the understanding of narrative art forms, such as literature and film, is what constitutes an acceptable interpretation of a work. One debate concerns whether there is a single correct interpretation of any work or multiple acceptable interpretations. Another concerns the constraints on acceptable interpretations, e.g., the extent to which the artist’s intentions may or should be taken into account.

Though these questions seem equally applicable to musical works (S. Davies 2002a; Dubiel 2011), most of the literature on understanding music has focused on two more specifically musical topics: first, our understanding of basic musical features, such as pitch and rhythm and, second, interpretations of works of the sort given by music theorists. (For more detailed introductions to these and other topics in musical understanding, see S. Davies 2011c and Huovinen 2011.)

Before we turn to those topics, it is worth noting that two distinct activities go by the name of “interpretation” in music (and other performance arts): what might be called performative and critical interpretation (Levinson 1993). While a critical interpretation of a musical work (often called an analysis) is roughly equivalent to an interpretation of a novel—typically expressed linguistically—a performative interpretation is a way of playing or singing the work, typically expressed in a performance of it. It is not easy to clarify the relationship between these two kinds of musical interpretation, but see Levinson 1993, Maus 1999, Thom 2007, Neufeld 2012, and Dodd 2020a.

Animals can hear music in a sense—your dog might be frightened by the loud noise emitted by your stereo. People, by contrast, can understand the music they hear. What constitutes this experience of understanding music? To use an analogy, while the mere sound of a piece of music might be represented by a sonogram, our experience of it as music is better represented by something like a marked-up score. We hear individual notes that make up distinct melodies, harmonies, rhythms, sections, and so on, and the interaction between these elements. Such musical understanding comes in degrees along a number of dimensions. Your understanding of a given piece or style may be deeper than mine, while the reverse is true for another piece or style. My general musical understanding may be narrow, in the sense that I only understand one kind of music, while you understand many different kinds (Budd 1985b: 233–5; S. Davies 2011c: 88–95). Moreover, different pieces or kinds of pieces may call on different abilities, since some music has no harmony to speak of, some no melody, and so on. Many argue that, in addition to purely musical features, understanding the emotions expressed in a piece is essential to adequately understanding it (e.g., Ridley 1993; S. Davies 1994; Levinson 1990d: 30; Scruton 1997; Robinson 2005: 348–78).

At the base of the musical experience seem to be (i) the experience of tones , as opposed to mere sounds of various frequencies, where a tone is heard as being in “musical space,” that is, as bearing relations to other tones such as being higher or lower, or of the same kind (at the octave), and (ii) the experience of movement , as when we hear a melody as leaping up or wandering far afield and then coming to rest where it began. Roger Scruton (1983; 1997: 1–96) argues that these experiences are irreducibly metaphorical, since they involve the application of spatial concepts to that which is not literally spatial. (There is no identifiable individual that moves from place to place in a melody (S. Davies 1994: 229–34).) Malcolm Budd (1985b) argues that to appeal to metaphor in this context is unilluminating since, first, it is unclear what it means for an experience to be metaphorical and, second, a metaphor is only given meaning through its interpretation, which Scruton not only fails to give, but argues is unavailable. Budd suggests that the metaphor is reducible, and thus eliminable, apparently in terms of purely musical (i.e., non-spatial) concepts or vocabulary. Stephen Davies (1994: 234–40) doubts that the spatial vocabulary can be eliminated, but he is sympathetic to Budd’s rejection of the centrality of metaphor. Instead, he argues that our use of spatial and motion terms to describe music is a secondary, but literal, use of those terms that is widely used to describe temporal processes, such as the ups and downs of the stock market, the theoretical position one occupies, one’s spirits plunging, and so on. The debate continues in Budd 2003, Scruton 2004, and S. Davies 2011d.

Davies is surely right about the ubiquity of the application of the language of space and motion to processes that lack individuals located in space. The appeal to secondary literal meanings, however, can seem as unsatisfying as the appeal to irreducible metaphor. We do not hear music simply as a temporal process, it might be objected, but as moving in the primary sense of the word, though we know that it does not literally so move. Andrew Kania (2015) develops a position out of this intuition by emphasizing Scruton’s appeal to imagination while dropping the appeal to metaphor, arguing that hearing the music as moving is a matter of imagining that its constituent sounds move. (See also de Clercq 2007 and Trivedi 2011: 116–18.) Kania explicitly models his theory on the popular Waltonian theory of fiction (K. Walton 1990), though Walton seems to resist the application of his theory to basic musical understanding because of the differences between music and more paradigmatically representational arts (K. Walton 1988: 358–9, 1994: 53–4).

Apart from pitch space and melodic movement, there has been little philosophical discussion of either the nature and understanding of basic musical features such as melody, rhythm, meter, and harmony or how these elements work together in complex musical wholes. But see Roger Scruton 1997: 19–79, 2007; Stephen Davies 2001: 47–71; Hamilton 2007: 119–52; and Cheyne, Hamilton, and Paddison 2020.

It is widely acknowledged that explicit music-theoretical knowledge can aid deeper musical understanding and is essential for the adequate description and understanding of musical experiences—including one’s own (Kivy 1990). However, several philosophers have argued that one need not possess these concepts explicitly (nor the correlative vocabulary) in order to listen with understanding (Budd 1985b; 245–8; S. Davies 1994: 346–9; Levinson 1990d: 35–41). Mark DeBellis (1995: 117–31) argues that understanding fairly basic features of music, such as different kinds of cadences, requires a fused experience in which one applies a concept such as dominant seventh in one’s perception of the musical sounds. Stephen Davies (2011c: 88–94) responds that the serious but untutored listener should be able to develop such concepts, and thus have such experiences. Erkki Huovinen (2008) provides an example intended to cast doubt on this. Suppose that a melody is transposed from C major to D-flat major, but in a lower octave. One listener might hear the melody as reappearing higher, since D-flat is a half-step above C, while another might hear it as lower, since the constituent pitches of the second appearance are all lower than those of the first. Only a listener who understands the sense in which both these claims are true—that the melody has been transposed down a major seventh—truly understands what is going on musically. Yet such concepts of pitch organization … are not usually learned without some tuition (Huovinen 2008: 325).

For various art-historical reasons, formalism was the dominant approach to music-theoretic analysis, that is, the critical interpretation of musical works, throughout the twentieth century. (Hamilton 2007: 66–94 & 153–91 provides a useful discussion of the history from a philosophical perspective.) In short, the value of works of music was held to reside primarily in their large-scale harmonic structure.

Jerrold Levinson (1997) makes a case against such “architectonicism” in favor of “concatenationism,” the view that basic musical understanding consists in following the musical and emotional qualities of passages of music, and transitions between them, that are short enough to be apprehended as a single experience (“quasi-hearing”). He qualifies this basic idea considerably, allowing for the experience of previous parts of the piece, and anticipation of future parts, to modify one’s experience of the music in the moment. He also allows that architectonic awareness may play a role in enhancing one’s moment-to-moment experience, and may even play an ineliminable part in the understanding of some pieces. Nonetheless, Levinson maintains that the part played by architectonic knowledge in basic musical understanding is minimal, and that the cases where architectonic knowledge is necessary are very much the exception.

Peter Kivy has taken up the gauntlet on behalf of architectonicism (2001; see also S. Davies 2011c: 95–9). While Kivy acknowledges that the kinds of experiences Levinson champions are necessary to basic musical understanding, he defends the idea that grasping the large-scale form of most pieces of Western classical music, at least, is necessary for an adequate understanding of them. He does not deny that the experience of the form of a piece in listening to it is more intellectual than quasi-hearing, but he rejects Levinson’s argument that it is non-perceptual, and thus marginal to an adequate experience of it as music. Rather, Kivy argues, such experience is a matter of bringing one’s perceptions under sophisticated concepts. (A tactic Kivy does not consider is an attempt to hoist Levinson with his own contextualist petard, arguing that even if architectonic listening is non-perceptual it is a well-established mode of understanding pieces of music in the Western classical music world, and thus that to argue music must be understood primarily perceptually is to beg the question.)

The extent of the disagreement between the architectonicist and the concatenationist is unclear. They agree that the aspect of musical understanding the other emphasizes is a non-negligible component in the full understanding of a musical work. Levinson has been explicit since the first publication of his view that he intends it more as a polemic against and corrective to architectonicism, rather than as a replacement for it (1997: ix–xi; 1999: 485; 2006a). Perhaps that purpose has now been fulfilled, but see Huovinen 2013 for a revival of the debate and an attempted synthesis.

5. Music and Value

Most philosophical discussions of the value of music are implicitly restricted to the artistic value of purely instrumental musical works. To the extent that such discussions are motivated by the abstract nature of such music (see below), it is not clear to what extent they can be extended to musical hybrids such as song. Moreover, as we saw in section 1.2, it is not obvious that all music is art. Perhaps non-art music can be artistically valuable, but it presumably has other values; a complete theory of the value of music would apparently have to account for those values. (Presumably, art music can also have non-artistic value.)

Following the literature, however, the remainder of this subsection considers the artistic value of purely musical works. This is not the place to go into the many disputes about the nature of aesthetic and artistic value. (For an excellent introduction, see Stecker 2003b.) For our purposes, we can note there are two central points about artistic value on which there is some consensus. First, most philosophers take the value of artworks to be intrinsic (or inherent ) to them, in the sense that the value of a work is tied essentially to the experience that the work affords. Thus, artworks are not (properly) valued merely instrumentally, as means to some end, but “for” or “in” themselves (Budd 1995: 1–16; S. Davies 1987: 198–200; Scruton 1997: 374–6; Levinson 1992: 15–17).

The question that naturally arises next is what it is about the experience an artwork affords that makes it valuable. That pleasure is a non-negligible part of the answer to this question is the second point upon which there is some consensus (S. Davies 1987: 198–205; Levinson 1992; Kivy 1997b: 212–17). However, concomitant with this consensus is an acknowledgment that simple pleasure taken, say, in the sensuousness of the musical sounds is too trivial to ground the great value widely attributed to music. In looking for other sources, the puzzle that arises is that music is supposed to be an abstract art, par excellence . If this means that music is divorced from everything else that concerns us in the “real world” (that is, extra-musical life), it is puzzling why we should find so valuable the experiences musical works afford.

There are a couple of dimensions to most solutions of the puzzle of pure music’s value. One is the extent to which it is agreed that music really is abstract. To the extent that one thinks that music is not divorced from the real world, one will be able to argue that music’s value is at least no more puzzling than the value of arts more obviously related to the real world, such as literature and representational painting and sculpture. The other dimension to most solutions of the puzzle of pure music’s value is the extent to which one thinks the abstractness of music is the source of its value. Thus, two theorists might agree on the extent to which music is related to the real world (by being expressive, say), yet one locate its primary value in that expressiveness while the other locates it in its abstract, purely formal features.

Unsurprisingly, those who take the experience of music’s expressiveness to be a more intimately emotional one (through being predicated on imaginative engagement with the music, say), tend to emphasize that experience as more central to musical understanding, and thus attribute a larger part of music’s value to its expressiveness. Those, on the other hand, whose theory of the experience of musical expressiveness is more distanced (a matter of noticed resemblance, say), tend to place less weight on this element in their theories of musical value. At one extreme of this spectrum is the position that denies music to be expressive at all, and thus cannot attribute any of music’s value to its expressiveness (most notably Hanslick 1854; see also Zangwill 2004). Most theorists agree, however, that music’s value is to be located in different kinds of experience, including the experience of formal and expressive features; their disagreements are mostly about the relative weight of these different kinds of experiences in a complete account of musical value.

The extent of the disagreement between various parties to this dispute is not clear. Those defending the value of music’s expressiveness tend to claim that its contribution to overall musical value is significant, but many stop short even of according it primary value, and do not argue against the value of formal elements of musical works (Ridley 1995: 192–6; Levinson 1982, 1992: 20–2, 1996a: 124–5; Robinson 2005: 413; Young 2014: 150–4). They content themselves rather with pointing out the ways in which expressiveness can be valuable, focusing largely on the value of the emotional responses such expressiveness elicits in us. These include many of the features discussed above with respect to our interest in listening to music that arouses negative affective states in the listener. To recap, our emotional responses to music’s expressiveness can enable us to savor, understand, and even (to some extent) experience emotions in a “safe” way. They can provide us with a cathartic release, and enable us to participate in a kind of communication with the composer or communion with other members of our musical culture (Levinson 1982, 1996a; Higgins 1991, 2012; S. Davies 1994: 271). Emphasizing this last point, Roger Scruton argues that music’s value is quasi-moral, in that the kinds of music one responds to, or those valued in a particular culture, reflect the state of that individual’s or culture’s “soul” (1997: 380–91; see also S. Davies 1994: 275–6.) Stephen Davies (1987: 207–12) has argued that there are beneficial consequences of an interest in music in general , such as heightened emotional and aural sensitivity, which are not properly valued as consequences of listening to individual pieces, but which lead us to value musical culture as a whole (just as we value kindness for its consequences in general, while rejecting instrumental motivations for kind acts as inappropriate).

By contrast, those who defend the value of formal features tend to argue that the value of those features is primary, and that the value of music’s expressiveness is overrated. Peter Kivy, for instance, argues that expressive properties serve merely to highlight musical structure, as color might be used by the painter to emphasize contour or mass. Other expressive properties serve as structural properties in their own right (1990: 196). (See also Sharpe 2000: 1–83, and Zangwill 2004.)

Alan Goldman (1992) argues against the idea that music is particularly suited to the expression of emotion, claiming that representational arts such as painting and literature are better at this. Moreover, he disputes the grounds of the value of expressiveness given above. For example, he denies that music can teach us much about the emotions, and that we can savor our negative emotional responses to expressive music. Similarly, after an extensive discussion of the nature of musical expressiveness, Malcolm Budd argues that such expressiveness cannot come close to explaining music’s value (1995: 155–7). He points to the facts that much valuable music is not expressive and that the equal expressiveness of different pieces would be outweighed in a comparative evaluation by the differences between them in terms of formal value.

Both Goldman and Budd locate the value of pure music precisely in the abstractness that to some seems the greatest obstacle to explaining that value. Budd (1995: 164–71) points out that we have an extensive interest in abstract forms outside the realm of music, such as those of natural formations and in the decorative arts, and that such forms are capable of possessing valued aesthetic properties, such as beauty, elegance, and so on. Thus, it is no surprise that we value highly the works of an art of abstract forms. Goldman (1992), by contrast, emphasizes the detachment from the world of practical affairs implied by music’s abstractness. The complexity of great musical works demands the active engagement of our cognitive faculties, which we find rewarding, yet not in the pursuit of some practical goal that could be frustrated.

These issues are thrown into sharp relief in the debate over how instrumental musical works could be “profound.” See Kivy 1990: 202–18, 1997b: 140–78, 2003; Levinson 1992; White 1992; Ridley 1995, 2004: 132–65; S. Davies 2002b; Dodd 2014b.

There is no space here to discuss the evaluation of musical works and performances. See S. Davies 1987, Levinson 1990e, and Gracyk 2011.

There are musical aspects or elements of many uncontroversially representational art forms, such as song. Jeanette Bicknell (2015: 81–91) and Aaron Smuts (2013) discusses the ethics of song performance. But there has been little discussion, in the analytic tradition, of the relationship between musical and ethical values (as opposed to musical examples of more general ethical concerns, such as cultural appropriation). Kathleen Higgins (1991, 2012) and Roger Scruton (1997: 457–508) argue in very different ways that music is – or should be – central to our thinking about ethics. Garry Hagberg has explored many connections between improvisatory jazz practice, ethics, and politics (2002, 2006, 2008, 2021; see also Higgins 1991: 177). Peter Kivy (2008) argues against music’s capacity to affect our moral knowledge, behavior, or character. Jerrold Levinson (2013: 51–5), Philip Alperson (2014), and James Harold (2016) defend music’s moral efficacy.

The debate over whether an artwork’s moral flaws are artistic flaws has focused almost exclusively on representational (especially narrative) art forms. (Gaut (2007) offers an excellent overview.) Music has largely been ignored because it has been assumed to lack sufficient representational capacity to embody an attitude toward some object. Maria José Alcaraz León (2012), however, argues that music’s emotional expressiveness is enough to apply arguments about whether moral flaws are artistic flaws to pure instrumental music. (For critical discussion, see Kania 2020: 254–5.) Musicologist Susan McClary argues that canonical works of instrumental classical music oppress women by expressing a positive attitude toward narratives of the subjection of feminine elements (e.g., certain musical themes) by masculine ones. (See, for example, McClary 1991: 19–23, 53–79; for critical discussion, see Maus (2011).)

Alcaraz León, María José, 2012, “Music’s Moral Character”, Teorema , 31(3): 179–91.
Alperson, Philip, 1984, “On Musical Improvisation”, Journal of Aesthetics and Art Criticism , 43(1): 17–29. doi:10.2307/430189
–––, 1998, “Improvisation: An Overview”, in Encyclopedia of Aesthetics (Volume 1), Michael Kelly (ed.), New York: Oxford University Press, pp. 478–9.
–––, 2009, “Facing the Music: Voices from the Margins”, Topoi , 28(2): 91–96. doi:10.1007/s11245-009-9052-9
–––, 2014, “Music and Morality”, in Ethics and the Arts , Paul Macneill (ed.), New York: Springer, pp. 21–31.
Alperson, Philip, Nguyen Chi Ben, & To Ngoc Thanh, 2007, “The Sounding of the World: Aesthetic Reflections on Traditional Gong Music of Vietnam”, Journal of Aesthetics and Art Criticism , 65(1): 11–20. doi:10.1111/j.1540-594X.2007.00233.x
Aristotle, Poetics , Stephen Halliwell (trans.), London: Duckworth, 1987.
Bartel, Christopher, 2017, “Rock as a Three-Value Tradition”, Journal of Aesthetics and Art Criticism , 75(2): 143–54.
Bicknell, Jeanette, 2015, Philosophy of Song and Singing: An Introduction , New York: Routledge.
Bicknell, Jeanette & John Andrew Fisher (eds.), 2013, Special Issue on Song, Songs, and Singing, Journal of Aesthetics and Art Criticism , 71(1).
Bordwell, David and Noël Carroll (eds.), 1996, Post-Theory: Reconstructing Film Studies , Madison, WI: University of Wisconsin Press.
Bresnahan, Aili, 2015, “Improvisation in the Arts”, Philosophy Compass , 10(9): 573–82. doi:10.1111/phc3.12251
Brown, Lee B., 1996, “Musical Works, Improvisation, and the Principle of Continuity”, Journal of Aesthetics and Art Criticism , 54(4): 353–69. doi:10.2307/431917
–––, 2000, “‘Feeling My Way’: Jazz Improvisation and its Vicissitudes—A Plea for Imperfection”, Journal of Aesthetics and Art Criticism , 58(2): 113–23. doi:10.2307/432090
–––, 2011, “Do Higher-Order Music Ontologies Rest on a Mistake?” British Journal of Aesthetics , 51(2): 169–84. doi:10.1093/aesthj/ayr002
–––, 2012, “Further Doubts about Higher-Order Ontology: Reply to Andrew Kania”, British Journal of Aesthetics , 52(1): 103–6. doi:10.1093/aesthj/ayr045
Bruno, Franklin, 2013, “A Case for Song: Against an (Exclusively) Recording-Centered Ontology of Rock”, Journal of Aesthetics and Art Criticism , 71(1): 65–74.
Budd, Malcolm, 1985a, Music and the Emotions: The Philosophical Theories , London: Routledge & Kegan Paul.
–––, 1985b, “Understanding Music”, Proceedings of the Aristotelian Society (Supplement), 59(1): 233–48. doi:10.1093/aristoteliansupp/59.1.215
–––, 1995, Values of Art: Pictures, Poetry and Music , London: Penguin.
–––, 2003, “Musical Movement and Aesthetic Metaphors”, British Journal of Aesthetics , 43(3): 209–23. doi:10.1093/bjaesthetics/43.3.209
Cameron, Ross P., 2008, “There Are No Things That Are Musical Works”, British Journal of Aesthetics , 48(3): 295–314. doi:10.1093/aesthj/ayn022
Caplan, Ben & Carl Matheson, 2004, “Can a Musical Work be Created?” British Journal of Aesthetics , 44(2): 113–34. doi:10.1093/bjaesthetics/44.2.113
–––, 2006, “Defending Musical Perdurantism”, British Journal of Aesthetics , 46(1): 59–69. doi:10.1093/aesthj/ayj004
Carroll, Noël, 1988, Mystifying Movies: Fads and Fallacies in Contemporary Film Theory , New York: Columbia University Press.
Cheyne, Peter, Andy Hamilton, & Max Paddison (eds.), 2019, The Philosophy of Rhythm: Aesthetics, Music, Poetics , New York: Oxford University Press.
Collingwood, R.G., 1938, The Principles of Art , Oxford: Oxford University Press.
Cooke, Deryck, 1959, The Language of Music , Oxford: Oxford University Press.
Cox, Renée, 1986, “A Defence of Musical Idealism”, British Journal of Aesthetics , 26(2): 133–142. doi:10.1093/bjaesthetics/26.2.133
Cray, Wesley D., 2019, “Transparent and Opaque Performance Personas”, Journal of Aesthetics and Art Criticism , 77(2): 181–91. doi:10.1111/jaac.12635
Cray, Wesley D. & Carl Matheson, 2017, “A Return to Musical Idealism”, Australasian Journal of Philosophy , 95(4): 702–15. doi:10.1080/00048402.2017.1281323
Currie, Gregory, 1989, An Ontology of Art , New York: St. Martin’s Press.
Davies, David, 2004, Art as Performance , Malden: Blackwell.
–––, 2009, “The Primacy of Practice in the Ontology of Art”, Journal of Aesthetics and Art Criticism , 67(2): 159–71. doi:10.1111/j.1540-6245.2009.01345.x
–––, 2011, Philosophy of the Performing Arts . Malden, MA: Wiley-Blackwell. doi:10.1002/9781444343458
–––, 2017, “Descriptivism and its Discontents”, Journal of Aesthetics and Art Criticism , 75(2): 117–29.
–––, 2021, “Puy on ‘Nested Types’”, Journal of Aesthetics and Art Criticism , 79(2): 251–5.
Davies, Stephen, 1987, “The Evaluation of Music”, in S. Davies 2003: 195–212.
–––, 1991, “The Ontology of Musical Works and the Authenticity of their Performances”, Noûs , 25(1): 21–41. doi:10.2307/2216091
–––, 1994, Musical Meaning and Expression , Ithaca, NY: Cornell University Press.
–––, 1997a, “John Cage’s 4′33″ : Is it Music?”, Australasian Journal of Philosophy , 75(4): 448–62. Reprinted in Davies 2003b: 11–29. doi:10.1080/00048409712348031
–––, 2001, Musical Works and Performances: A Philosophical Exploration , Oxford: Oxford University Press.
–––, 2002a, “The Multiple Interpretability of Musical Works”, in Is There a Single Right Interpretation? , Michael Krausz (ed.), University Park: Pennsylvania State University Press, 231–50.
–––, 2002b, “Profundity in Instrumental Music”, British Journal of Aesthetics , 42(4): 343–56. doi:10.1093/bjaesthetics/42.4.343
–––, 2003a, “Ontologies of Musical Works”, in S. Davies 2003b: 30–46.
–––, 2003b, Themes in the Philosophy of Music , Oxford: Oxford University Press.
–––, 2006, “Artistic Expression and the Hard Case of Pure Music”, in Kieran 2006: 179–91.
–––, 2007, “Balinese Aesthetics”, Journal of Aesthetics and Art Criticism , 65(1): 21–29. doi:10.1111/j.1540-594X.2007.00234.x
–––, 2011b, “Emotional Contagion from Music to Listener”, in S. Davies 2011e: 47–65.
–––, 2011c, “Musical Understandings”, in S. Davies 2011e: 88–129.
–––, 2011d, “Music and Metaphor”, in S. Davies 2011e: 21–34.
–––, 2011e, Musical Understandings and Other Essays on the Philosophy of Music , Oxford: Oxford University Press.
–––, 2012, “On Defining Music”, The Monist , 95: 535–55.
DeBellis, Mark, 1995, Music and Conceptualization , Cambridge: Cambridge University Press.
De Clercq, Rafael, 2007, “Melody and Metaphorical Movement”, British Journal of Aesthetics , 47(2): 156–68. doi:10.1093/aesthj/ayl053
Dewey, John, 1934, Art as Experience , New York: Minton, Balch.
Dodd, Julian, 2007, Works of Music: An Essay in Ontology , Oxford: Oxford University Press.
–––, 2010, “Confessions of an Unrepentant Timbral Sonicist”, British Journal of Aesthetics , 50(1): 33–52. doi:10.1093/aesthj/ayp044
–––, 2013, “Adventures in the Metaontology of Art: Local Descriptivism, Artefacts and Dreamcatchers”, Philosophical Studies , 165(3): 1047–68. doi:10.1007/s11098-012-9999-z
–––, 2014a, “Upholding Standards: A Realist Ontology of Standard Form Jazz”, Journal of Aesthetics and Art Criticism , 72(3): 277–90.
–––, 2014b, “The Possibility of Profound Music”, British Journal of Aesthetics , 54(3): 299–322. doi:10.1093/aesthj/ayu031
–––, 2018, “What 4′33″ Is”, Australasian Journal of Philosophy , 96(4): 629–41.
–––, 2020a, Being True to Works of Music , Oxford: Oxford University Press.
–––, 2020b, “Blurred Lines: Ravasio on ‘Historically Informed Performance’”, Journal of Aesthetics and Art Criticism , 78(1): 85–90.
Dodd, Julian, & Philip Letts, 2017, “Types, Tokens, and Talk about Musical Works”, Journal of Aesthetics and Art Criticism , 75(3): 249–63.
Dubiel, Joseph, 2011 “Analysis”, in Gracyk & Kania 2011: 535–45.
Dyck, John, 2014, “Perfect Compliance in Musical History and Musical Ontology”, British Journal of Aesthetics , 54(1): 31–47.
Fisher, John Andrew, 1998, “Rock ’n’ Recording: The Ontological Complexity of Rock Music”, Musical Worlds: New Directions in the Philosophy of Music , Philip Alperson (ed.), University Park, PA: Pennsylvania State University Press. 109–23.
–––, 2018, “Jazz and Musical Works”, Journal of Aesthetics and Art Criticism , 76(2): 151–62. doi:10.1111/jaac.12450
Gaut, Berys, 2007, Art, Emotion and Ethics , Oxford: Oxford University Press.
Goehr, Lydia, 1992, The Imaginary Museum of Musical Works: An Essay in the Philosophy of Music , Oxford: Oxford University Press.
–––, 1998, The Quest for Voice: On Music, Politics, and the Limits of Philosophy: The 1997 Ernest Bloch Lectures , Oxford: Oxford University Press.
Goldman, Alan, 1992, “The Value of Music”, Journal of Aesthetics and Art Criticism , 50(1): 35–44. doi:10.2307/431065
–––, 1995, “Emotions in Music (A Postscript)”, Journal of Aesthetics and Art Criticism , 53(1): 59–69. doi:10.2307/431737
Goodman, Nelson, 1968, Languages of Art , Indianapolis: Hackett; second edition, 1976.
Gould, Carol & Kenneth Keaton, 2000, “The Essential Role of Improvisation in Musical Performance”, Journal of Aesthetics and Art Criticism , 58(2): 143–8. doi:10.2307/432093
Gracyk, Theodore, 1996, Rhythm and Noise: An Aesthetics of Rock , Durham, NC: Duke University Press.
–––, 2001, I Wanna Be Me: Rock Music and the Politics of Identity , Philadelphia: Temple University Press.
–––, 2009, “The Song Remains the Same, But Not Always,” in Led Zeppelin and Philosophy: All will be Revealed , Scott Calef (ed.), Chicago: Open Court, 31–45.
–––, 2011, “Evaluating Music”, in Gracyk & Kania 2011: 165–75.
–––, 2017, “Performer, Persona, and the Evaluation of Musical Performance”, Contemporary Aesthetics , 15(13). [ Gracyk 2017 available online ]
Gracyk, Theodore & Andrew Kania (eds.), 2011, The Routledge Companion to Philosophy and Music , New York: Routledge.
Hagberg, Garry L., 1998, “Improvisation: Jazz Improvisation”, in Encyclopedia of Aesthetics (Volume 1), Michael Kelly (ed.), New York: Oxford University Press, 479–82.
–––, 2002, “On Representing Jazz: An Art Form in Need of Understanding”, Philosophy and Literature , 26(1): 188–98. doi:10.1353/phl.2002.0013
–––, 2006 “Jazz Improvisation: A Mimetic Art?”, Revue Internationale de Philosophie , 238: 469–85.
–––, 2008, “Jazz Improvisation and Ethical Interaction: A Sketch of the Connections”, in Art and Ethical Criticism , Garry L. Hagberg (ed.), Malden, MA: Blackwell, pp. 259–85.
–––, 2021, “Jazz”, in The Oxford Handbook of Western Music and Philosophy , Tomás McAuley, Nanette Nielsen, Jerrold Levinson, & Ariana Phillips-Hutton (eds.), Oxford: Oxford University Press, pp. 579–99. doi:10.1093/oxfordhb/9780199367313.013.28
Hamilton, Andy, 2007, Aesthetics and Music , New York: Continuum.
Hanslick, Eduard, [1854] 1986, On the Musically Beautiful: A Contribution towards the Revision of the Aesthetics of Music ( Vom Musikalisch-Schönen ), Geoffrey Payzant (trans.), Indianapolis, IN: Hackett. (First published 1854.)
Harold, James, 2016, “On the Ancient Idea that Music Shapes Character”, Dao , 15: 341–54. doi:10.1007/s11712-016-9515-9
Hazlett, Allan, 2012, “Indication, Abstraction, and Individuation”, in Mag Uidhir 2012a: 161–78.
Higgins, Kathleen Marie, 1991, The Music of Our Lives , Philadelphia: Temple University Press.
–––, 2007, “An Alchemy of Emotion: Rasa and Aesthetic Breakthroughs”, Journal of Aesthetics and Art Criticism , 65(1): 43–54. doi:10.1111/j.1540-594X.2007.00236.x
–––, 2012, The Music Between Us: Is Music a Universal Language? , Chicago: University of Chicago Press.
Howell, Robert, 2002, “Types, Indicated and Initiated”, British Journal of Aesthetics , 42(2): 105–27. doi:10.1093/bjaesthetics/42.2.105
Hume, David, [1757] 1985, “Of Tragedy”, in Essays: Moral, Political, and Literary , E. Miller (ed.), Indianapolis: Liberty Classics, pp. 216–25.
Huovinen, Erkki, 2008, “Levels and Kinds of Listeners’ Musical Understanding”, British Journal of Aesthetics , 48(3): 315–37. doi:10.1093/aesthj/ayn023
–––, 2011, “Understanding Music”, in Gracyk & Kania 2011: 123–33.
–––, 2013, “Concatenationism and Anti-architectonicism in Musical Understanding”, Journal of Aesthetics and Art Criticism , 71(3): 247–60. doi:10.1111/jaac.12018
Ingarden, Roman, [1961] 1986, The Work of Music and the Problem of its Identity ( Utwór muzyczny i sprawa jego tożsamości ), Adam Czerniawski (trans.), Berkeley, CA: University of California Press; first published in German in 1961.
Jackendoff, Ray, 2011, “Music and Language”, in Gracyk & Kania 2011: 101–12.
Kania, Andrew, 2006, “Making Tracks: The Ontology of Rock Music”, Journal of Aesthetics and Art Criticism , 64(4): 401–14. doi:10.1111/j.1540-594X.2006.00219.x
–––, 2008, “The Methodology of Musical Ontology: Descriptivism and Its Implications”, British Journal of Aesthetics , 48(4): 426–44. doi:10.1093/aesthj/ayn034
–––, 2010, “Silent Music”, Journal of Aesthetics and Art Criticism , 68(4): 343–53. doi:10.1111/j.1540-6245.2010.01429.x
–––, 2011a, “Definition”, in Gracyk & Kania 2011: 1–13.
–––, 2011b, “All Play and No Work: An Ontology of Jazz”, Journal of Aesthetics and Art Criticism , 69(4): 391–403. doi:10.1111/j.1540-6245.2011.01483.x
–––, 2015, “An Imaginative Theory of Musical Space and Movement”, British Journal of Aesthetics , 55(2): 157–72. doi:10.1093/aesthj/ayu100
–––, 2020, Philosophy of Western Music: A Contemporary Introduction , New York: Routledge. doi:10.4324/9781315210629
–––, 2022, “The Heart of Classical Work-Performance”, The British Journal of Aesthetics , 62(1): 125–141, doi:10.1093/aesthj/ayab031
Kieran, Matthew (ed.), 2006, Contemporary Debates in Aesthetics and the Philosophy of Art , Malden, MA: Blackwell.
Kivy, Peter, 1980, The Corded Shell: Reflections on Musical Expression , Princeton, NJ: Princeton University Press.
–––, 1983a, “Platonism in Music: A Kind of Defense”, Grazer Philosophische Studien , 19(1): 109–129. Reprinted in Kivy 1993: 35–58. doi:10.1163/18756735-90000194
–––, 1983b, “Platonism in Music: Another Kind of Defense”, American Philosophical Quarterly , 24(3): 245–252. Reprinted in Kivy 1993: 59–74.
–––, 1988a, “Orchestrating Platonism”, in Aesthetic Distinction , Thomas Anderberg, Tore Nilstun, & Ingmar Persson (eds.), pp. 42–55. Reprinted in Kivy 1993: 75–94.
–––, 1988b, Osmin’s Rage: Philosophical Reflections on Opera, Drama, and Text , Princeton: Princeton University Press. (Second edition with a new preface and final chapter, Ithaca, NY: Cornell University Press, 1999.)
–––, 1989, Sound Sentiment: An Essay on the Musical Emotions, including the complete text of “The Corded Shell” , Philadelphia: Temple University Press.
–––, 1990, Music Alone: Philosophical Reflections on the Purely Musical Experience , Ithaca, NY: Cornell University Press.
–––, 1993, The Fine Art of Repetition: Essays in the Philosophy of Music , Cambridge: Cambridge University Press.
–––, 1994, “Speech, Song, and the Transparency of Medium: A Note on Operatic Metaphysics”, Journal of Aesthetics and Art Criticism , 52(1): 63–68. doi:10.2307/431585
–––, 1995, Authenticities: Philosophical Reflections on Musical Performance , Ithaca, NY: Cornell University Press.
–––, 1997a, “Music in the Movies: A Philosophical Enquiry”, in Film Theory and Philosophy , R. Allen & M. Smith (eds.), Oxford: Oxford University Press. 308–28.
–––, 1997b, Philosophies of Arts: An Essay in Differences , Cambridge: Cambridge University Press.
–––, 1999, “Feeling the Musical Emotions”, British Journal of Aesthetics , 39(1): 1–13. doi:10.1093/bjaesthetics/39.1.1
–––, 2001, “Music in Memory and Music in the Moment ”, in New Essays on Musical Understanding , Oxford: Oxford University Press, pp. 183–217.
–––, 2002, Introduction to a Philosophy of Music , Oxford: Oxford University Press.
–––, 2003, “Another Go at Musical Profundity: Stephen Davies and the Game of Chess”, British Journal of Aesthetics , 43(4): 401–11. doi:10.1093/bjaesthetics/43.4.401
–––, 2008, “Musical Morality”, Revue Internationale de Philosophie , 238: 397–412.
Kleinschmidt, Shieva & Jacob Ross, 2012, “Repeatable Artwork Sentences and Generics”, in Mag Uidhir 2012a: 125–57.
Langer, Susanne K., 1953, Feeling and Form , London: Routledge & Kegan Paul.
Lerdahl, Fred & Ray S. Jackendoff, 1983, A Generative Theory of Tonal Music , Cambridge, MA: MIT Press.
Letts, Philip, 2015, “Against Kania’s Fictionalism about Musical Works”, British Journal of Aesthetics , 55(2): 209–24. doi:10.1093/aesthj/ayu102
–––, 2018, “The Property Theory of Musical Works”, Journal of Aesthetics and Art Criticism , 76(1): 57–69.
Levinson, Jerrold, 1980, “What a Musical Work Is”, Journal of Philosophy , 77(1): 5–28. doi:10.2307/2025596
–––, 1982, “Music and Negative Emotion.” Reprinted in Levinson 1990f: 306–35.
–––, 1984, “Hybrid Art Forms”, The Journal of Aesthetic Education , 18(4): 5–13. doi:10.2307/3332623
–––, 1987, “Song and Music Drama”, in Levinson 1990f: 42–59.
–––, 1990a, “The Concept of Music”, in Levinson 1990f: 267–78.
–––, 1990b, “What a Musical Work Is, Again”, in Levinson 1990f: 215–63.
–––, 1990c, “Authentic Performance and Performance Means”, in Levinson 1990f: 393–408.
–––, 1990d, “Musical Literacy”, The Journal of Aesthetic Education , 24(1): 17–30. doi:10.2307/3332852; reprinted in Levinson 1996c: 27–41 [page reference is to the reprint].
–––, 1990e, “Evaluating Musical Performance”, in Levinson 1990f: 376–92.
–––, 1990f, Music, Art, and Metaphysics , Ithaca, NY: Cornell University Press.
–––, 1992, “Pleasure and the Value of Works of Art”, British Journal of Aesthetics , 32(4): 295–306. Reprinted in Levinson 1996c: 11–24. doi:10.1093/bjaesthetics/32.4.295
–––, 1993, “Performative vs Critical Interpretation in Music”, The Interpretation of Music , Michael Krausz (ed.), Oxford : Clarendon Press. 33–60. Reprinted in Levinson 1996c: 60–89.
–––, 1996a, “Musical Expressiveness”, in Levinson 1996c: 90–125.
–––, 1996b, “Film Music and Narrative Agency”, in Bordwell & Carroll 1996: 254–88. Reprinted in Contemplating Art: Essays in Aesthetics , Oxford: Oxford University Press, 2006, pp. 143–83.
–––, 1996c, The Pleasures of Aesthetics , Ithaca, NY: Cornell University Press.
–––, 1997, Music in the Moment , Ithaca, NY: Cornell University Press.
–––, 1999, “Reply to commentaries on Music in the Moment ”, Music Perception , 16(4): 485–94. doi:10.2307/40285805
––– (ed.), 2003, The Oxford Handbook of Aesthetics , Oxford: Oxford University Press. doi:10.1093/oxfordhb/9780199279456.001.0001
–––, 2006a, “Concatenationism, Architectonicism, and the Appreciation of Music”, Revue Internationale de Philosophie , 238: 505–14.
–––, 2006b, “Musical Expressiveness as Hearability-as-expression”, in Kieran 2006: 192–206.
–––, 2012, “Indication, Abstraction, and Individuation”, in Mag Uidhir 2012: 49–61. doi:10.1093/acprof:oso/9780199691494.003.0003
–––, 2013, “Popular Song as Moral Microcosm: Life Lessons from Jazz Standards”, Royal Institute of Philosophy Supplement , 71: 51–66. doi:10.1017/S1358246112000264
Love, Stefan Caris, 2016, “The Jazz Solo as Virtuous Act”, Journal of Aesthetics and Art Criticism , 74(1): 61–74. doi:10.1111/jaac.12238
Magnus, P.D., 2012, “Historical Individuals Like Anas platyrhynchos and ‘Classical Gas’”, in Mag Uidhir 2012a: 108–24.
–––, 2016, “Kind of Borrowed, Kind of Blue”, Journal of Aesthetics and Art Criticism , 74(2): 179–85.
–––, 2022, A Philosophy of Cover Songs , Cambridge: Open Book. doi:10.11647/OBP.0293
Mag Uidhir, Christy (ed.), 2012a, Art and Abstract Objects , Oxford: Oxford University Press. doi:10.1093/acprof:oso/9780199691494.001.0001
–––, 2012b, “Introduction: Art, Metaphysics, and the Paradox of Standards”, in Mag Uidhir 2012a: 1–26.
Matheson, Carl & Ben Caplan, 2011, “Ontology”, in Gracyk & Kania 2011: 38–47.
Matravers, Derek, 1998, Art and Emotion , Oxford: Clarendon. doi:10.1093/acprof:oso/9780199243167.001.0001
–––, 2011, “Arousal Theories”, in Gracyk & Kania 2011: 212–22.
Maus, Fred Everett, 1999, “Musical Performance as Analytical Communication”, in Performance and Authenticity in the Arts , Salim Kemal & Ivan Gaskell (eds.), Cambridge: Cambridge University Press, pp. 129–53. doi:10.1017/CBO9780511520051.007
–––, 2011, “Music and Gender”, in Gracyk & Kania 2011: 569–80.
McAuley, Tomás, Nanette Nielsen, Jerrold Levinson, & Ariana Phillips-Hutton (eds.), 2021, The Oxford Handbook of Western Music and Philosophy , Oxford: Oxford University Press. doi:10.1093/oxfordhb/9780199367313.001.0001
McClary, Susan, 1991, Feminine Endings: Music, Gender, and Sexuality , Minneapolis: University of Minnesota Press.
McKeown-Green, Jonathan, 2014, “What is Music? Is There a Definitive Answer?” Journal of Aesthetics and Art Criticism , 72(4): 393–403.
Meyer, Leonard B., 1956, Emotion and Meaning in Music , Chicago, IL: University of Chicago Press.
Millikan, Ruth Garrett, 1984, Language, Thought, and Other Biological Categories: New Foundations for Realism , Cambridge, MA: MIT Press.
–––, 2000, On Clear and Confused Ideas: An Essay about Substance Concepts , Cambridge: Cambridge University Press.
Moruzzi, Caterina, 2018, “Every Performance Is a Stage”, Journal of Aesthetics and Art Criticism , 76(3): 341–51.
Neufeld, Jonathan A., 2012, “Critical Performances”, Teorema , 31(3): 89–104.
Nussbaum, Charles O., 2007, The Musical Representation: Meaning, Ontology, and Emotion , Cambridge, MA: MIT Press.
–––, 2021, “Ontology”, in The Oxford Handbook of Western Music and Philosophy , Tomás McAuley, Nanette Nielsen, Jerrold Levinson, & Ariana Phillips-Hutton (eds.), Oxford: Oxford University Press, pp. 325–344. doi:10.1093/oxfordhb/9780199367313.013.17
Penner, Nina, 2020, Storytelling in Opera and Musical Theater , Bloomington, IN: Indiana University Press.
Predelli, Stefano, 1995, “Against Musical Platonism”, British Journal of Aesthetics , 35(4): 338–50. doi:10.1093/bjaesthetics/35.4.338
–––, 1999a, “Goodman and the Score”, British Journal of Aesthetics , 39(2): 138–47. doi:10.1093/bjaesthetics/39.2.138
–––, 1999b, “Goodman and the Wrong Note Paradox”, British Journal of Aesthetics , 39(4): 364–75. doi:10.1093/bjaesthetics/39.4.364
–––, 2001, “Musical Ontology and the Argument from Creation”, British Journal of Aesthetics , 41(3): 279–92. doi:10.1093/bjaesthetics/41.3.279
–––, 2009, “Ontologese and Musical Nihilism”, British Journal of Aesthetics , 49(2): 179–83. doi:10.1093/aesthj/ayp004
Puy, Nemesio García-Carril, 2019, “The Ontology of Musical Versions: Introducing the Hypothesis of Nested Types”, Journal of Aesthetics and Art Criticism , 77(3): 241–54.
Radford, Colin, “Muddy Waters”, Journal of Aesthetics and Art Criticism , 49(3): 247–52. doi:10.2307/431480
Ravasio, Matteo, 2019a, “Historically Uninformed Views of Historically Informed Performance”, Journal of Aesthetics and Art Criticism , 77(2): 193–205.
–––, 2019b, “Appearance Emotionalism in Music: Analysis and Criticism”, Journal of Aesthetic Education , 53(3): 93–105.
–––, 2020, “Historically Informed Performance: A Reply to Dodd”, Journal of Aesthetics and Art Criticism , 78(1): 90–4.
Ridley, Aaron, 1993, “Bleeding Chunks: Some Remarks about Musical Understanding”, Journal of Aesthetics and Art Criticism , 51(4): 589–96. doi:10.2307/431891
–––, 1995, Music, Value and the Passions , Ithaca, NY: Cornell University Press.
–––, 2003, “Expression in Art”, in Levinson 2003: 211–27.
–––, 2004, The Philosophy of Music: Theme and Variations , Edinburgh: Edinburgh University Press.
Robinson, Jenefer, 1994, “The Expression and Arousal of Emotion in Music”, Journal of Aesthetics and Art Criticism , 52(1): 13–22. doi:10.2307/431581
–––, 2005, Deeper than Reason: Emotion and its Role in Literature, Music, and Art , Oxford: Oxford University Press.
–––, 2011, “Expression Theories”, in Gracyk & Kania 2011: 201–11.
Rohrbaugh, Guy, 2003, “Artworks as Historical Individuals”, European Journal of Philosophy , 11(2): 177–205. doi:10.1111/1468-0378.00182
Rudner, Richard, 1950, “The Ontological Status of the Esthetic Object”, Philosophy and Phenomenological Research , 10(3): 380–8.
Sartre, Jean-Paul, [1940] 1948, The Psychology of Imagination ( L’imaginaire ), Translator unrecorded. New York, NY: Philosophical Library.
Scruton, Roger, 1983, “Understanding Music”, in The Aesthetic Understanding: Essays in the Philosophy of Art and Culture , Manchester: Carcanet Press, pp. 77–100.
–––, 1997, The Aesthetics of Music , Oxford: Oxford University Press.
–––, 2004, “Musical Movement: A Reply to Budd”, British Journal of Aesthetics , 44(2): 184–7. doi:10.1093/bjaesthetics/44.2.184
–––, 2007, “Thoughts on Rhythm”, in Stock 2007: 226–55.
Sharpe, R.A., 1982, Review of Peter Kivy, The Corded Shell , British Journal of Aesthetics , 22(1): 81–2. doi:10.1093/bjaesthetics/22.1.81
–––, 2000, Music and Humanism: An Essay in the Aesthetics of Music , Oxford: Oxford University Press.
Smith, Jeff, 1996, “Unheard Melodies? A Critique of Psychoanalytic Theories of Film Music”, in Bordwell & Carroll 1996: 230–47.
Smuts, Aaron, 2013, “The Ethics of Singing Along: The Case of ‘Mind of a Lunatic’”, Journal of Aesthetics and Art Criticism , 71(1): 121–9. doi:10.1111/j.1540-6245.2012.01548.x
Stecker, Robert, 2003a, Interpretation and Construction: Art, Speech, and the Law , Malden, MA: Blackwell.
–––, 2003b, “Value in Art”, in Levinson 2003: 307–24.
–––, 2009, “Methodological Questions about the Ontology of Music”, Journal of Aesthetics and Art Criticism , 67(4): 375–86. doi:10.1111/j.1540-6245.2009.01367.x
Sterritt, David, 2000, “Revision, Prevision, and the Aura of Improvisatory Art”, Journal of Aesthetics and Art Criticism , 58(2): 163–72. doi:10.2307/432095
Stock, Kathleen (ed.), 2007, Philosophers on Music: Experience, Meaning, and Work , Oxford: Oxford University Press.
Thom, Paul, 2007, The Musician as Interpreter , University Park, PA: Pennsylvania State University Press.
Thomasson, Amie L., 2004, “Roman Ingarden”, The Stanford Encyclopedia of Philosophy , (Spring 2004 Edition), E. N. Zalta (ed.), URL = < https://plato.stanford.edu/archives/spr2004/entries/ingarden/ >.
–––, 2006, “Debates about the Ontology of Art: What Are We Doing Here?” Philosophy Compass , 1(3): 245–55. doi:10.1111/j.1747-9991.2006.00021.x
Tillman, Chris, 2011, “Musical Materialism”, British Journal of Aesthetics , 51(1): 13–29. doi:10.1093/aesthj/ayq028
Tolstoy, Leo, [1898] 1995, What is Art? ( Chto takoe iskusstvo ), Richard Pevear & Larissa Volokhonsky (trans.), London: Penguin.
Tormey, Alan, 1971, The Concept of Expression: A Study in Philosophical Psychology and Aesthetics , Princeton: Princeton University Press.
Trivedi, Saam, 2002, “Against Musical Works as Eternal Types”, British Journal of Aesthetics , 42(1): 73–82. doi:10.1093/bjaesthetics/42.1.73
–––, 2011 “Music and Imagination”, in Gracyk & Kania 2011: 113–22.
Urmson, J. O., 1973, “Representation in Music”, in Philosophy and the Arts (Royal Institute of Philosophy Lectures, Volume 6), London: Palgrave, 132–46. doi:10.1017/S0080443600000844
Valone, James J., 1985, “Musical Improvisation as Interpretive Activity”, Journal of Aesthetics and Art Criticism , 44(2): 193–4. doi:10.2307/430522
Wadle, Douglas, forthcoming, “Organized Sound, Sounds Heard, and Silence”, Ergo .
Walton, Kendall L., 1988, “What is Abstract about the Art of Music?”, Journal of Aesthetics and Art Criticism , 46(3): 351–64. doi:10.2307/431106
–––, 1990, Mimesis as Make-Believe: On the Foundations of the Representational Arts , Cambridge, MA: Harvard University Press.
Walton, Susan Pratt, 2007, “Aesthetic and Spiritual Correlations in Javanese Gamelan Music”, Journal of Aesthetics and Art Criticism , 65(1): 31–41. doi:10.1111/j.1540-594X.2007.00235.x
White, David A., 1992, “Toward a Theory of Profundity in Music”, Journal of Aesthetics and Art Criticism , 50(1): 23–34.
Wollheim, Richard, 1968, Art and its Objects , Cambridge: Cambridge University Press; second edition (with six supplementary essays), 1980. doi:10.1017/CBO9781316286777
Wolterstorff, Nicholas, 1980, Works and Worlds of Art , Oxford: Clarendon Press.
–––, 1987, “The Work of Making a Work of Music”, in What is Music? An Introduction to the Philosophy of Music , Philip Alperson (ed.), New York: Haven. 101–29.
Young, James O., 1988, “The Concept of Authentic Performance”, British Journal of Aesthetics , 28(3): 228–38. doi:10.1093/bjaesthetics/28.3.228
–––, 2014, Critique of Pure Music , Oxford: Oxford University Press.
Young, James O. & Carl Matheson, 2000, “The Metaphysics of Jazz”, Journal of Aesthetics and Art Criticism , 58(2): 125–34. doi:10.2307/432091
Zangwill, Nick, 2004, “Against Emotion: Hanslick Was Right About Music”, British Journal of Aesthetics , 44(1): 29–43. doi:10.1093/bjaesthetics/44.1.29
–––, 2007, “Music, Metaphor, and Emotion”, Journal of Aesthetics and Art Criticism , 65(4): 391–400. doi:10.1111/j.1540-594X.2007.00272.x

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Definition of thesis

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In high school, college, or graduate school, students often have to write a thesis on a topic in their major field of study. In many fields, a final thesis is the biggest challenge involved in getting a master's degree, and the same is true for students studying for a Ph.D. (a Ph.D. thesis is often called a dissertation ). But a thesis may also be an idea; so in the course of the paper the student may put forth several theses (notice the plural form) and attempt to prove them.

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Word History

in sense 3, Middle English, lowering of the voice, from Late Latin & Greek; Late Latin, from Greek, downbeat, more important part of a foot, literally, act of laying down; in other senses, Latin, from Greek, literally, act of laying down, from tithenai to put, lay down — more at do

14th century, in the meaning defined at sense 3a(1)

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the sins of the fathers are visited upon the children

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Music, Definitions of

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Collection: 20th Anniversary of ISMIR

Overview articles

An interdisciplinary review of music performance analysis.

Alexander Lerch
Claire Arthur
Siddharth Gururani
Music Performance Analysis

1. Introduction

Music, as a performing art, requires a performer or group of performers to render a musical “blueprint” into an acoustic realization ( Hill, 2002 ; Clarke, 2002b ). This musical blueprint can, for example, be a score as in the case of Western classical music, a lead sheet for jazz, or some other genre-dependent representation describing the compositional content of a piece of music. The performers are usually musicians but might also be, e.g., a computer rendering audio.

The performance plays a major role in how listeners perceive a piece of music: even if the blueprint is identical for different renditions, as is the case in Western classical music, listeners may prefer one performance over another and appreciate different ‘interpretations’ of the same piece of music. These differences are the result of the performers’ intentionally or unintentionally interpreting, modifying, adding to, and dismissing information from the score or blueprint (for the sake of simplicity, the remainder of this text will use the terms score and blueprint synonymously). This constant re-interpretation of music is inherent to the art form and is a vital and expected component of music.

Formally, musical communication can be described as a chain as shown in Figure 1 : the composer typically only communicates with the listener through the performer who renders the blueprint to convey musical ideas to the listener ( Kendall and Carterette, 1990 ). The performer does this by varying musical parameters while leaving the compositional content untouched. The visualized model of the communication chain displays a one-way communication path of information transmission. There can be, however, flows of information in the other direction, influencing the performance itself. Such a feedback path might transport information such as the instrument’s sound and vibration ( Todd 1993 ), the room acoustics, ( Luizard et al., 2020 ), and the audience reaction ( Kawase, 2014 ). Although the recording studio lacks an audience, a performance can also evolve during a recording session. Katz ( 2004 ) points out that in such a session, performers will listen to the recording of themselves and adjust “aspects of style and interpretation.” In addition, the producer might also have impact on the recorded performance ( Maempel, 2011 ).

Chain of musical communication from composer to listener. The dotted box disappears for performances that are not recorded.

The large variety of performance scenarios makes it necessary to focus on the common core of all music performances: the audio signal. While a music performance can, for example, contain visual information such as gestures and facial expressions ( Bergeron and Lopes, 2009 ; Platz and Kopiez, 2012 ; Tsay, 2013 ), not every music performance has these cues. A musical robot, for example, may or may not convey such cues. The acoustic rendition, however, is the integral part of a music performance that cannot be missing; simply put, there exists no music without sound. An audio recording is a fitting representation of the sound that allows for quantitative analysis. This audio-focused view should neither imply that non-audio information cannot be an important part of a performance nor that non-audio information cannot be analyzed. The focus on the audio recording of a performance makes it important to recognize that every recording contains processing choices and interventions by the production team with potential impact on the expressivity of the recording. Maempel ( 2011 ) discusses as main influences in the context of classical music the sound engineer (dynamics, timbre, panorama, depth) and the editor (splicing of different tracks, tempo and timing, pitch). These manipulations and any restrictions of the recording and distribution medium are part of the audio to be analyzed and cannot be separated anymore from the performers’ creation. As the release of a recording usually has to be pre-approved by the performers it is assumed that the performers’ intent has not been distorted.

Although the change of performance parameters can have a major impact on a listener’s perception of the music ( Clarke, 2002a, also Section 4 ), the nature of the performance parameter variation is often subtle, and must be evaluated in reference to something; typically either the same performer and piece at a different time, different performances of the same piece, or deviations from a perfectly quantized performance rendered from a symbolic score representation (e.g., MIDI) without tempo or dynamics variations. Notice that this reference problem makes the genre of music for performance analysis biased towards classical music, as there is commonly an abstract (i.e., score) representation that easily fills the model of the quantized version of a piece of music. In addition, the classical model of performance is based on a finite number of pre-composed musical works performed by numerous individuals over time, thus making it a rich resource for performance analysis. Figure 2 visualizes two example piano performances of the beginning of Frédéric Chopin’s Fantasie in F Minor, Op. 49 , where variations in local tempo, dynamics, and pedaling can easily be identified. Even in musical traditions that also have scores, such as jazz, the role of the performer as ‘interpreter’ of the score is complicated by normative practices such as elaborate embellishment and improvisation (often to the point of highly obscuring the original material). Other genres might completely eliminate the concept of separable composition and performance. McNeil ( 2017 ) argues, for example, that the distinction between ‘composition’ and ‘improvisation’ cannot capture the essence of performance creativity in Indian Hindustani music, where the definition of fixed ‘composed’ material and improvised material becomes hard and it might be more meaningful to refer to seed ideas which grow and expand throughout the performance.

Variations in two different performances of Frédéric Chopin’s Fantasie in F Minor, Op. 49 (taken from the Maestro dataset ( Hawthorne et al., 2019 ). For each performance, timing and dynamics are shown using the piano rolls (darker color indicates higher velocity). Pedal control is shown below the piano roll (darker color indicates increasing usage of the pedal). Visualized excerpts correspond to the beginning of the piece.

Although the distinction between score and performance parameters is less obvious for non-classical genres of Western music, especially ones without clear separation between the composer and the performer, the concept and role of a performer as interpreter of a composition is still very much present, be it as a live interpretation of a studio recording or a cover version of another artist’s song. In these cases, the freedom of the performers in modifying the score information is often much higher than it is for classical music — reinterpreting a jazz standard can, for example, include the modification of content related to pitch, harmony, and rhythm.

Formally, performance parameters can be structured in the same basic categories that we use to describe musical audio in general: tempo and timing, dynamics, pitch, and timbre ( Lerch, 2012 ). While the importance of different parameters might vary from genre to genre, the following list introduces some mostly genre-agnostic examples to clarify these performance parameter categories:

Tempo and Timing : the score specifies the general rhythmic content, just as it often contains a tempo indicator. While the rhythm is often only slightly modified by performers in terms of micro-timing, the tempo (both in terms of overall tempo as well as expressive tempo variation) is frequently seen only as a suggestion to the performer.
Dynamics : in most cases, score information on dynamics is missing or only roughly defined. The performers will vary loudness and highlight specific events with accents based on their plan for phrasing and tension, and the importance of certain parts of the score.
Pitch : the score usually defines the general pitches to play, but pitch-based performance parameters include expressive techniques such as vibrato as well as intentional or unintentional choices for intonation.
Timbre : as the least specific category of musical parameters, scores encode timbre often only implicitly (e.g., instrumentation) while performers can, for example, change playing techniques or the choice of specific instrument configurations (such as the choice of organ registers).

There exist many performance parameters and playing techniques that either cannot be easily associated with one of the above categories or span multiple categories; examples of such parameters are articulation ( legato, staccato, pizzicato ) or forms of ornamentation in Baroque or jazz music.

One intuitive form of Music Performance Analysis (MPA) —discussing, criticizing, and assessing a performance after a concert— has arguably taken place since music was first performed. Traditionally, however, such reviews are qualitative and not empirical. While there are a multitude of approaches to music performance analysis, and numerous factors shape the insights and outcomes of such analyses, in this literature review we are only concerned with quantitative, systematic approaches to MPA scholarship. However, it is easily acknowledged that the design of any empirical study and the interpretation of results could be improved by a careful consideration of the musicological context (e.g., the choice of edition that a classical performance is rendered from — see Rink ( 2003 )). Nevertheless, a detailed discussion of such contexts is beyond the scope of this article.

Early attempts at systematic and empirical MPA can be traced back to the 1930s with vibrato and singing analysis by Seashore ( 1938 ) and the examination of piano rolls by Hartmann ( 1932 ). In the past two decades, MPA has greatly benefited from the advances in audio analysis made by members of the Music Information Retrieval (MIR) community, significantly extending the volume of empirical data by simplifying access to a continuously growing heritage of commercial audio recordings. While advances in audio content analysis have had clear impact on MPA, the opposite is less true. An informal search reveals that while there have been publications on performance analysis at ISMIR, the major MIR conference, their absolute number remains comparably small (compare Toyoda et al., 2004 ; Takeda et al., 2004 ; Chuan and Chew, 2007 ; Sapp, 2007 , 2008 ; Hashida et al., 2008 ; Liem and Hanjalic, 2011 ; Okumura et al., 2011 ; Devaney et al., 2012 ; Jure et al., 2012 ; Van Herwaarden et al., 2014 ; Liem and Hanjalic, 2015 ; Arzt and Widmer, 2015 ; Page et al., 2015 ; Xia et al., 2015 ; Bantula et al., 2016 ; Peperkamp et al., 2017 ; Gadermaier and Widmer, 2019 ; Maezawa et al., 2019 with a title referring to “music performance” out of approximately 1,950 ISMIR papers overall ).

Historically, MIR researchers often do not distinguish between score-like information and performance information even if the research deals with audio recordings of performances. For instance, the goal of music transcription, a very popular MIR task, is usually to transcribe all pitches with their onset times ( Benetos et al., 2013 ); that means that a successful transcription system transcribes two renditions of the same piece of music differently, although the ultimate goal is to detect the same score (note that this is not necessarily true for all genres). Therefore, we can identify a disconnect between MIR research and performance research that impedes both the evolution of MPA approaches and robust MIR algorithms, slows gaining new insights into music aesthetics, and hampers the development of practical applications such as new educational tools for music practice and assessment. This paper aims at narrowing this gap by introducing and discussing MPA and its challenges from an MIR perspective. In pursuit of this goal, this paper complements previous review articles on music performance research ( Sloboda, 1982 ; Palmer, 1997 ; Gabrielsson, 1999 , 2003 ; Goebl et al., 2005 ) and expands on Lerch et al. ( 2019 ) by integrating non-classical and non-Western music genres, including a more extensive number of relevant publications, and clearly outlining the challenges music performance research is facing. While performance research has been inclusive of various musical genres, such as the Jingju music of the Beijing opera ( Zhang et al., 2017 ; Gong, 2018 ), traditional Indian music ( Clayton, 2008 ; Gupta and Rao, 2012 ; Narang and Rao, 2017 ) and jazz music ( Abeßer et al., 2017 ), the vast majority of studies are concerned with Western classical music. The focus on Western music can also be observed in the field of MIR in general, despite efforts in diversifying the field ( Serra, 2014 ; Tzanetakis, 2014 ). As mentioned, the reason for the focus on classical music within MPA may be due to the clear systematic differentiation between score and performance. This imbalance means that this overview article will necessarily emphasize Western classical music while referring to other musical styles wherever appropriate.

The remainder of this paper is structured as follows. The following Section 2 presents research on the objective description, modeling, and visualization of the performance itself, identifying commonalities and differences between performances. The subsequent sections focus on studies taking these objective performance parameters and relating them to either the performer (Section 3), the listener (Section 4), or the assessment of the performer from a listener’s perspective (Section 5). We conclude our overview with a summary on applications of MPA and final remarks in Section 6.

2. Performance Measurement

A large body of work focuses on an exploratory approach to analyzing performance recordings and describing performance characteristics. Such studies typically extract characteristics such as the tempo curve or histogram ( Repp, 1990 ; Palmer, 1989 ; Povel, 1977 ; Srinivasamurthy et al., 2017 ) or loudness curve ( Repp, 1998a ; Seashore, 1938 ) from the audio and aim at either gaining general knowledge on performances or comparing attributes between different performances/performers based on trends observed in the extracted data. Additionally, there are also studies focusing on discovery of general patterns in performance parameters, which can be useful in identifying trends such as changes over eras ( Ornoy and Cohen, 2018 ).

2.1 Tempo, timing, and dynamics

Deviations in tempo, timing, and dynamics are considered to be some of the most salient performance parameters and hence have been the focus of various studies in MPA. While these performance parameters have been studied in isolation in some instances, we present them together since there is substantial work aiming to understand their interrelation.

Close relationships were observed between musical phrase structure and deviations in tempo and timing ( Povel, 1977 ; Shaffer, 1984 ; Palmer, 1997 ). For example, tempo changes in the form of ritardandi tend to occur at phrase boundaries ( Palmer, 1989 ; Lerch, 2009 ). As a related structural cue, Chew ( 2016 ) proposed the concept of tipping points in the score, leading to a timing deviation with extreme pulse variability in the context of Western classical music performance.

Correlations were observed between timing and dynamics patterns ( Repp, 1996b ; Lerch, 2009 ). Dalla Bella and Palmer ( 2004 ) found that the overall tempo influences the overall loudness of a performance. There are also indications that loudness can be linked to pitch height ( Repp, 1996b ). Cheng and Chew ( 2008 ) analyzed global phrasing strategies for violin performance using loudness and tempo variation profiles and found dynamics to be more closely related to phrasing than tempo. While the close relation of tempo and dynamics to structure has been repeatedly verified, Lerch ( 2009 ) did not succeed in finding similar relationships between structure and timbre properties in the case of string quartet recordings.

In the context of jazz performances, Wesolowski ( 2016 ) found that both score and performance parameters such as underlying harmony, pitch interval size, articulation, and tempo had significant correlations with timing variations between successive eighth notes. He also found these parameters correlated with synchronicity between separate parts of a jazz ensemble. Several researchers conducted experiments studying swing style jazz ( Ellis, 1991 ; Prögler 1995 ; Collier and Collier 2002 ; Friberg and Sundström, 2002 ). Such studies focused on measuring or quantifying discrepancies and asynchrony of performers in order to study what characteristics of jazz performances made them ‘swing’ ( Friberg and Sundström, 2002 ). Ellis ( 1991 ) found that asynchrony of jazz swing performers to the prevailing meter is positively correlated with the tempo and consists mainly of delaying attacks. Prögler ( 1995 ) noted that participatory asynchrony in swing is observed and measurable at a subsyntax level. Abeßer et al. ( 2014a ) studied the relationship of note dynamics in jazz improvisation with other contextual information such as note duration, pitch, and position in the score. They used a score-informed music source separation algorithm to isolate the solo instrument and found that higher and longer notes tend to be louder, and structural accents are typically emphasized. Busse ( 2002 ) conducted an experiment to objectively measure deviations in terms of timing, articulation, and dynamics of jazz swing performers from mechanical regularity using MIDI-based ‘groove quantization.’ He created reference performer models using the measured performance parameter deviations and compared them against mechanical or quantized models. Experts were asked to rate the ‘swing representativeness’ of the different models. He found that reference performer models were rated to be representative of swing whilst similar mechanical models of performance were rated poorly. Ashley ( 2002 ) described timing in jazz ballad performance as melodic rhythm flexibility over a strict underlying beat pattern, which is a type of rubato. He found timing deviations to have strong relationships with musical structure. Collier and Collier ( 1994 ) studied tempo in corpora of jazz performances. They manually timed these recordings and found that tempo was normally (Gaussian) distributed when computed in terms of metronome markings but not when computed using note durations. They noted that while jazz performances are stable in terms of tempo, systematic patterns in timing variabilities tend to serve expressive functions. Iyer ( 2002 ) contrasted micro-timing in African-American music from techniques in Western classical music such as rubato and ritardando . Employing an embodied cognition framework, he argued that the differences are due to the emphasis of the human body in the cultural aesthetics of African-American musics.

Studies analyzing musical timing are not only limited to Western music. Srinivasamurthy et al. ( 2017 ) performed a large-scale computational analysis of rhythm in Hindustani classical percussion, confirming and quantifying tendencies pertaining to timing such as deviations of tempo within a metric cycle (also referred to as tal ). The study demonstrated the value of using MIR techniques for rhythm analysis of large corpora of music. Bektaş ( 2005 ) confirmed the relationship between prosodic meter arûz and musical meter usûl in Turkish vocal music and found the existence of an even stronger concordance between a subset of prosodic patterns called bahir and usûl .

In addition to the studies already presented, there is a large body of work that focuses on modeling of timing, tempo, and dynamics. Section 2.3 discusses such modeling approaches to performance measurement in detail.

2.2 Tonal analysis

Pitch-based performance parameters have been analyzed mostly in the context of single-voiced instruments. For instance, the vibrato range and rate has been studied for vocalists ( Seashore, 1938 ; Devaney et al., 2011 ) and violinists ( Bowman Macleod, 2006 ; Dimov, 2010 ). Regarding intonation, Devaney et al. ( 2011 ) found significant differences between professional and non-professional vocalists in terms of the size of the interval between semi-tones.

Studies have also been conducted on the relationship between pitch and meter in the context of bebop style jazz ( Järvinen, 1995 ; and Toiviainen, 2000 ). These studies found that measurements of chorus-level tonal hierarchies match quite closely to rating profiles of chromatic pitches found in European art music and that metrical structure plays a role in determining which pitches are emphasized or de-emphasized. The studies also indicated that there is no effect of syncopation or polyrhythm on the use of certain pitches.

Franz ( 1998 ) demonstrated the utility of Markov chains in the analysis of jazz improvisation. He found Markov chains useful in quantifying the frequency of notes and patterns of notes which are in turn useful as comparison tools for musical scale analyses. Furthermore, he noted that this modeling technique can be useful for stylistic comparison as well as in developing metrics for style and creativity. Frieler et al. ( 2016 ) proposed an analysis framework for jazz improvisation based on so-called ‘midlevel units’ (MLU). These are musical units on the middle level between individual notes and larger form parts. They hypothesize that MLUs correspond to the improvisers’ playing ideas and musical ideas and propose a taxonomy system for MLUs. The authors subsequently study the distribution of occurrences and durations of MLUs in a large corpus of jazz improvisations. They note that the most common MLUs used in improvisations belong to the lick and line categories. They also find that the distribution of MLU types differs between performers and styles.

Research on jazz solos has utilized MIR methods for source separation and pitch tracking to analyze intonation and other tonal features ( Abeßer et al., 2014c , 2015 ). Abeßer et al. ( 2014c ) proposed a score-informed pitch tracking algorithm for analysis. They analyze distributions of various pitch contour features to identify patterns between different artists and instruments. Several contextual parameters such as relative position of notes in a phrase, beat position in the bar, etc., are also extracted and correlations between these parameters and pitch features are studied. They found statistically significant correlations between the pitch contour features and contextual features but most of the correlations had small effect size. Abeßer et al. ( 2015 ) utilized score-based source separation and pitch tracking to study intonation in jazz brass and woodwind solos to identify trends in tuning frequency over various decades, intonation for different artists, and properties of vibrato depending on context and performer.

A survey of computational methods utilized to study tonality in Turkish Makam music was conducted by Bozkurt et al. ( 2014 ), including research related to the analysis of tuning frequency and melodic phrases, transcription of performances, Makam recognition, as well as rhythmic and timbral analysis in Makam music. Atli et al. ( 2015 ) discuss the importance of tonic frequency or karar estimation for Makam music and devise a simple method for the task: they find that detecting the last note of a performance recording and estimating the frequency works well for karar estimation. Hakan et al. ( 2012 ) analyzed Turkish ney performances to identify key aspects of embellishments. They found that the rate of change of vibrato and ‘pitch bump’, which measures the deviation of pitch just before ascending or descending into the next note, were the key features useful for distinguishing performance styles across various performers.

Similarly, several researchers have worked on analysis of melody and tonality in Indian classical music. Ganguli and Rao ( 2017 ) modeled ungrammatical phrases, i.e., phrases straying away from the predetermined raga , in Hindustani music performance. They utilized computational techniques to model the tonal hierarchies and melodic shapes of different ragas toward that end. Viraraghavan et al. ( 2017 ) analyzed the use of ornamentation known as gamakas in Carnatic music performances. They find that the use of gamakas is vital in defining the raga during a performance.

Chen ( 2013 ) developed methods for analysis of intonation in Beijing opera or Jingju music. The methods, involving peak distribution analysis of pitch histograms, validated claims in literature that the fourth degree is higher, and the seventh degree is lower than the corresponding pitches in the equally tempered scale. Chen also found pitch histograms to be good features to distinguish role types in opera performances. Caro Repetto et al. ( 2015 ) utilized MIR techniques for pitch tracking and audio feature extraction to compare singing styles of two Jingju schools, namely the Mei and Cheng schools. Their experiments quantitatively support observations made in musicological texts about characteristics such as pitch register, vibrato, volume/dynamics, and timbre brightness. In addition, they find other properties not previously reported; for example, vibrato in Mei tends to be slower and wider on average than in Cheng . Yang et al. ( 2015 ) developed methods based on filter diagonalization and hidden Markov models to detect and model vibrato and portamento in performances of erhu , violin and Jingju opera vocals. There are studies aiming to understand the relationship between linguistic tone and melodic pitch contours in Jingju music by utilizing machine learning methods such as clustering ( Zhang et al., 2015 , 2014 ). Jingju music utilizes a two-dialect tone system making the tone-melody relationship complicated.

2.3 Modeling

While most of the studies mentioned above make use of statistical methods to extract, summarize, visualize, and investigate patterns in the performance data, researchers have also investigated modeling approaches to better understand performances. Several overview articles exist covering research on generating expressive musical performance ( Cancino-Chacón et al., 2018 ; Kirke and Miranda, 2013 ; Widmer and Goebl, 2004 ). In this subsection, however, we primarily focus on methods that model performance parameters leading to useful insights, while ignoring the generative aspect.

Several researchers have attempted to model timing variations in performances. In early work, Todd ( 1995 ) modeled ritardandi and accelerandi using kinematics theory, concluding that tempo variation is analogous to velocity. Li et al. ( 2017 ) introduced an approach invariant to phrase length for analyzing expressive timing. They utilize Gaussian mixture models (GMMs) to model the polynomial regression coefficients for tempo curves instead of directly modeling expressive timing. Liem and Hanjalic ( 2011 ) proposed an entropy-based deviation measure for quantifying timing in piano performances and found it to be a good alternative to standard-deviation-based measures. Grachten et al. ( 2017 ) utilized recurrent neural networks to model timing in performances and demonstrated the benefits over static models that do not account for temporal dependencies between score features. Stowell and Chew ( 2013 ) introduced a Bayesian model of tempo modulation at various time-scales in performances.

For dynamics modeling, Kosta et al. ( 2015 ) applied and compared two change-point algorithms to detect dynamics changes in performed music, evaluating them using the corresponding dynamics markers in the score. In further research, Kosta et al. ( 2018 ) quantified the relationships between notated and performed dynamics using a corpus of performed Chopin Mazurkas. Kosta et al. ( 2016 ) applied various machine learning methods such as decision trees, support vector machines (SVM) and neural networks to understand the relationship between dynamics markings and performed loudness. They find that score-based features are more important than performer style features for predicting dynamics markings given performed loudness and vice versa. Similarly, Marchini et al. ( 2014 ) utilized decision trees, SVMs and k-nearest neighbor classifiers to model and predict performance features such as intensity, timing deviations, vibrato extent, and bowing speed of each note in string quartet performances. They found that inter-voice attributes played a strong role in models trained with ensemble recordings versus solo recordings. Grachten and Widmer ( 2012 ) introduced a so-called linear basis function model which encodes score information using weighted combinations of a set of basis functions. They utilize this model to predict and analyze dynamics in performance. Grachten et al. ( 2017 ) extended the framework with a recurrent model which better captures temporal relationships in order to improve modeling of timing. Cancino-Chacón et al. ( 2017 ) performed a large-scale evaluation of linear, non-linear and temporal models for dynamics in piano and orchestral performances. These models utilize various features extracted from the score to encode structure in dynamics, pitch and rhythm.

An alternative direction in performance modeling research involves methods to discover rules for performance ( Widmer, 2003 ). Widmer’s ensemble learning method succeeds in finding simple, and in some cases novel, rules for music performance. An approach to modeling expressive jazz performance based on genetic algorithms was proposed by Ramirez et al. ( 2008 ), learning rules to generate sequences that are best able to fit the training data.

2.4 Visualization

Many traditional approaches to performance parameter visualization such as pitch contours ( Gupta and Rao, 2012 ; Abeßer et al., 2014c ), tempo curves ( Repp, 1990 ; Palmer, 1989 ; Povel, 1977 ), and scatter plots ( Lerch, 2009 ) are not necessarily interpretable or easily utilized for comparative studies. This led researchers to develop other, potentially more intuitive or condensed forms of visualization that allow describing and comparing different performances. The ‘performance worm’, for example, is a pseudo-3D visualization of the tempo-loudness space that allows the identification of specific gestures in that space ( Langner and Goebl, 2002 ; Dixon et al., 2002 ). Sapp ( 2007 , 2008 ) proposed the so-called ‘timescapes’ and ‘dynascapes’ to visualize subsequent similarities of performance parameters.

The ‘Phenicx’ project explored various ways of visualizing orchestral music information, including both score and performance information ( Gasser et al., 2015 ). Dynagrams and tempograms, for example, are used to visualize various temporal levels of loudness and tempo variations, respectively. Dittmar et al. ( 2018 ) devised swingogram representations by analyzing and tracking the swing ratio implied by the ride cymbal in jazz swing performance. This visualization enables insights into jazz improvisation such as the interaction between a soloist and the drummer.

2.5 Challenges

The studies presented in this section often follow an exploratory approach; extracting various parameters in order to identify commonalities or differences between performances. While this is, of course, of considerable interest, one of the main challenges is the interpretability of these results. Just because there is a timing difference between two performances does not necessarily mean that this difference is musically or perceptually meaningful. Without this link, however, results can only provide limited insights into which parameters and parameter variations are ultimately important.

The acquisition of data for analysis is another challenge in MPA research. Goebl et al. ( 2005 ) discuss various methods that have been used to that end, including special instruments (such as Yamaha Disklaviers, piano rolls), hand measurement of performance parameters, as well as automatic audio analysis tools. All these methods have potential downsides. For example, the use of special instruments and sensors excludes the analysis of performances not recorded on these specific devices and the associated formats (e.g., MIDI) may have difficulties representing special playing techniques. Manual annotation can be time consuming and tedious. The fact that the majority of studies surveyed here rely on manually annotated data implies that available algorithms for automatic performance parameter extraction lack the reliability and/or accuracy for practical MPA tasks. This is especially true for ensemble performances where the polyphonic and poly-timbral nature as well as timing fluctuations between individual voices complicate the analysis. As a result of these challenges, most studies are performed on manually annotated data with small sample sizes, possibly leading to poor generalizability of the results. The increasing number of datasets providing performance data as listed in Table 1 , however, gives hope that this ceases to be an issue in the future.

A list of datasets useful for various tasks within MPA.

name	reference	instruments	genre	data	size	performance parameters

APL	Winters et al. ( )	piano	classical	audio	621 recordings	piano practice
CBFdataset	Wang et al. ( )	bamboo flute	chinese	audio	1GB	playing techniques
CrestMusePEDB	Hashida et al. ( )	piano	classical	xml	121 performances	timing, dynamics
CSD	Cuesta et al. ( )	vocals	classical	audio, f0 series	48 recordings	intonation
DAMP	–	vocals	popular	audio	24874 recordings (14 songs)	singing
DrumPT	Wu and Lerch ( )	drums	popular	audio	30 recordings	playing techniques
Duet	Xia and Dannenberg ( )	piano	classical	MIDI	105 performances	timing, dynamics
EEP	Marchini et al. ( )	string quartet	classical	audio	23 recordings	timing, gestures, bowing techniques
Erkomaishvili	Rosenzweiget al. ( )	vocals	Georgian	audio, f0 series, MusicXML	116 recordings	timing, pitch
Groove MIDI	Gillick et al. ( )	drums	popular	MIDI	13.6 hours	drum timing
GPT	Su et al. ( )	guitar	popular	audio	6580 recordings	playing techniques
IDMT-SMT-Bass	Abeßer et al. ( )	bass	popular	audio	3.6 hours	playing techniques
IDMT-SMT-Guitar	Kehling et al. ( )	guitar	popular	audio	4700 note events	playing techniques
Intonation	Wager et al. ( )	vocals	popular	audio, f0 series	4702 performances	singing
Jingju-Pitch	Gong et al. ( )	vocals	Beijing Opera	f0 series	13MB	intonation
JKU-ScoFo	Henkel et al. ( )	piano	classical	audio, MIDI	16 performances	timing, dynamics
Kara1k	Bayle et al. ( )	vocals	popular	audio	1000 songs	singing
Maestro	Hawthorne et al. ( )	piano	classical	audio, MIDI	200 hours	timing, dynamics
MASTmelody	Bozkurt et al. ( )	vocals	–	f0 series	1018 recordings	pass/fail ratings
MASTrhythm	Falcao et al. ( )	percussion	–	audio	3721 recordings	pass/fail ratings
Mazurka	Sapp ( )	piano	classical	beat markers	2732 recordings	tempo, dynamics
PGD	Sarasúa et al. ( )	piano	classical	audio, video, MIDI	210 recordings	gestures, intentions
QUARTET	Papiotis ( )	string quartet	classical	audio, video	96 recordings	timing, gestures, bowing techniques
SMD	Müller et al. ( )	piano	classical	audio, MIDI	50 performances	timing, dynamics
SUPRA	Shi et al. ( )	piano	classical	piano rolls, MIDI	478 performances	gestures, timing, dynamics
URMP	Li et al. ( )	multi	classical	audio, video	44 pieces	timing, dynamics
VGD	Sarasúa et al. ( )	violin	classical	audio, EMG, IMU	960 recordings	position data, playing techniques
Vienna 4x22	Goebl ( )	piano	classical	audio, MIDI	4 pieces, 22 pianists	timing, dynamics
VocalSet	Wilkins et al. ( )	vocals	popular	audio	6GB	singing techniques
WJazzD	Pfleiderer et al. ( )	wind instruments	jazz	MIDI	456 solos	timing, pitch

3. Performer

While most studies focus on the extraction of performance parameters or the mapping of these parameters to the listeners’ perception (see Sections 4 and 5), some investigate the capabilities, goals, and strategies of performers. A performance is usually based on an explicit or implicit performance plan with clear intentions ( Clarke, 2002b ). This seems to be the case also for improvised music: for instance, Dean et al. ( 2014 ) could verify clearly perceivable structural boundaries in free jazz piano improvisation. There is, as Palmer verified, a clear relation between reported intentions and objective parameters related to phrasing and timing of the performance ( Palmer, 1989 ). Similar relations between the intended emotionality and loudness and timing measures were reported in multiple studies ( Juslin, 2000 ; Dillon, 2001 , 2003 , 2004 ). For example, projected emotions such as anger and sadness show significant correlations with high and low tempo, and high and low overall sound level, respectively. Moreover, a performer’s control of expressive variation has been shown to significantly improve the conveyance of emotion. For instance, a study by Vieillard et al. ( 2012 ) found that listeners were better able to perceive the presence of specific emotions in music when the performer played an ‘expressive’ (as opposed to a mechanical) rendition of the composition. This suggests that the performer plays a fairly large role in communicating an emotional ‘message’ above and beyond what is communicated through the score alone ( Juslin and Laukka, 2003 ). In music performed from a score in particular, the score-based representation might be thought of as a set of instructions in the sense that the notational system itself is used to communicate basic structural information to the performer. However, as noted by Rink ( 2003 ), the performer is not simply a medium or vessel through which performance directions are carried out, but “what performers do has the potential to impart meaning and create structural understanding.”

Research by Friberg and Sundström ( 2002 ) set out to tackle the question of what makes music ‘swing.’ Their approach was to examine the variation in the ‘swing ratio’ between pairs of eighth notes in jazz music, and they found that it tends to vary as a function of tempo. This finding has interesting implications for MPA as well as perceptual experiments. Across performers there is a clear systematic relation between the stretching of the ratio at slower tempi and the compressing of the ratio at higher tempi, such that it approaches a 1:1 ratio at approximately 300 BPM. Interestingly, the duration of the second eighth note remained fairly constant at approximately 100 ms for medium to fast tempi, suggesting a practical limit on tone duration that, as the authors speculate, could be due to perceptual factors.

Another interesting area of research is performer error. Repp ( 1996a ) analyzed performers’ mistakes and found that errors were concentrated in mostly unimportant parts of the score (e.g., middle voices) where they are harder to recognize ( Huron, 2001 ), suggesting that performers intentionally or unintentionally avoid salient mistakes.

3.1 Influences

In addition to the performance plan itself, there are other influences shaping the performance. Acoustic parameters of concert halls such as the early decay time have been shown to impact performance parameters such as tempo ( Schärer Kalkandjiev and Weinzierl, 2013 , 2015 ; Luizard et al., 2019 , 2020 ). Related work by Repp showed that pedaling characteristics in piano performance are dependent on the overall tempo ( Repp, 1996c , 1997b ).

Other studies investigate the importance of the feedback of the music instrument to the performer ( Sloboda, 1982 ); there have been studies reporting on the effect of deprivation of auditory feedback ( Repp, 1999 ), investigating the performers’ reaction to delayed or changed auditory feedback ( Pfordresher and Palmer, 2002 ; Finney and Palmer, 2003 ; Pfordresher, 2005 ), or evaluating the role of tactile feedback in a piano performance ( Goebl and Palmer, 2008 ). In summary, the different forms of feedback have been found to have small but significant impact on reproduction accuracy of performance parameters.

3.2 Challenges

There is a wealth of information about performances that can be learned from performers. The main challenge of this direction of inquiry is that such studies have to involve the performers themselves. This limits the amount of available data and usually excludes well-known and famous artists, resulting in a possible lack of generalizability. Depending on the experimental design, the separation of possible confounding variables (for example, motor skills, random variations, and the influence of common performance rules) from the scrutinized performance data can be a considerable challenge.

4. Listener

Every performance will ultimately be heard and processed by a listener. The listener’s meaningful interpretation of the incoming musical information depends on a sophisticated network of parameters. These parameters include both objective (or, at least, measurable) features that can be estimated from a score or derived from a performance, as well as subjective and ‘internal’ ones such as factors shaped by the culture, training, and history of the listener. Presently, there remain many acoustic parameters related to music performance where the listener’s response has not been measured, either in terms of perceptibility or aesthetic response or both. For this reason, listener-focused MPA remains one of the most challenging and elusive areas of research. However, to the extent that MPA research and its applications depend on perceptual information (e.g., perceived expressiveness), or intend to deliver perceptually-relevant output (e.g., performance evaluation or reception, similarity ratings), it is imperative to achieve a fuller understanding of the perceptual relevance of the manipulation and interaction of performance characteristics (e.g., tempo, dynamics, articulation). The subsequent paragraphs provide a brief overview of the relevant literature on music perception and MPA, along with some discussion of the relevance of this information for current and future work in both MPA and in MIR in general.

4.1 Musical expression

When it comes to listener judgments of a performance, it remains poorly understood which aspects are most important, salient, or pertinent for the listener’s sense of satisfaction. According to Schubert and Fabian ( 2014 ), listeners are very concerned with the notion of ‘expressiveness’ which is a complex, multifaceted construct. Performance expression is commonly defined as “variations in musical parameters by a signal or instrumentalist” ( Dibben, 2014 ). In other words, performance expression implies the intentional application of systematic variation on the part of the performer. On the other hand, expressive performance (or ‘expressiveness’) implies a judgment (either implicit or explicit) on the part of a listener.

As stated by Devaney ( 2016 ), however, not all variation is expressive: “The challenge […] is determining which deviations are intentional, which are due to random variation, and which are due to specific physical constraints that a given performer faces, such as bio-mechanical limitations […]. In regard to physical limitations, these deviations may be both systematic and observable in collected performance data, but may not be perceptible to listeners.” Thus, identifying the variation in a performance that would be intended as expressive is only the first step. Discovering which performance characteristics contribute to an expressive performance requires dissecting what listeners deem ‘expressive’ as well as understanding the relation and potential differences between measured and perceived performance features.

Expressiveness is genre and style dependent, meaning that the perceived appropriate level and style of expression in a pop ballad will be different from a jazz ballad, and that expression in a Baroque piece will be different from that of a Romantic piece —something that has been referred to as ‘stylishness’ ( Fabian and Schubert, 2009 ; Kendall and Carterette, 1990 ). For example, the timing difference between the primary melody and the accompaniment tends to be wider in jazz than in classical music, and there is evidence that the direction of difference is reversed, i.e., the melody leads the accompaniment in classical piano music ( Goebl, 2001 ; Palmer, 1996 ) while it follows in the case of jazz ( Ashley, 2002 ). Similarly, syncopation created by anticipating the beat is normative in pop genres but appears to be reversed in jazz music where syncopation is created by delaying the onset of the melody ( Dibben, 2014 ).

In addition to style-related expression, there is the perceived amount of expressiveness, which is considered independent of stylishness ( Schubert and Fabian, 2006 ). Finally, Schubert and Fabian ( 2014 ) distinguish a third ‘layer’ of expressiveness, emotional expressiveness , which arises from a performer’s manipulation of various features specifically to alter or enhance emotion. This is distinct from musical expressiveness, or expressive variation, which more generally refers to the manipulation of compositional elements by the performer in order to be ‘expressive’ without necessarily needing to express a specific emotion. Practically speaking, however, it may be difficult for listeners to separate these varieties of expressiveness ( Schubert and Fabian, 2014, p.293 ), and research has demonstrated that there are interactions between them (e.g., Vieillard et al., 2012 ).

4.2 Expressive variation

Several scholars have made significant advances in our understanding of the role of timing, tempo, and dynamic variation on listeners’ perception of music. As noted in Section 2, the subtle variations in tempo and dynamics executed by a performer have been shown to play a large role in highlighting and segmenting musical structure. For instance, the perception of metrical structure is largely mediated through changes in timing and articulation within small structural units such as the measure, beat, or sub-beat, whereas the perception of formal structures are largely communicated through changes across larger segments such as phrases (e.g., Sloboda, 1983 ; Gabrielsson, 1987 ; Palmer, 1996 ; Behne and Wetekam, 1993 ). An experiment by Sloboda ( 1983 ) found that listeners were better able to identify the meter of an ambiguous passage when performed by a more experienced performer. This suggests that even subtle changes in articulation and timing—more easily executed by an expert performer—play an important role in communicating structural information to the listener. Through measuring the differences in the performers’ expressive variations, Sloboda identified dynamics and articulation —in particular, a tenuto articulation— as the most important features for communicating which notes were accented.

The extent to which a listener’s musical expectations align with a performer’s expressive variations appears an important consideration. For example, because of the predictable relation between timing and structural segmentation, it has been demonstrated that listeners find it difficult to detect timing (and duration) deviations from a ‘metronomic’ performance when the pattern and placement of those deviations are stylistically typical ( Repp, 1990 , 1992 ; Ohriner, 2012 ). Likewise, Clarke ( 1993 ) found pianists able to more accurately reproduce a performance when the timing profile was ‘normative’ with regards to the musical structure, and also found listeners’ aesthetic judgments to be highest for those performances with the original timing profiles compared with those that were inverted or altered.

In addition to communicating structural information to the listener, performance features such as timing and dynamics have also been studied extensively for their role in contributing to a perceived ‘expressive’ performance (see Clarke, 1998 ; Gabrielsson, 1999 ). For instance, a factor analysis by Schubert and Fabian ( 2014 ) examined the features and qualities that may be related to perceived expressiveness, finding that dynamics had the highest impact on the factor labeled ‘emotional expressiveness.’ Recent work by Battcock and Schutz ( 2019 ) showed attack rate to be the most important predictor of intensity (or “arousal” in terms of two-dimensional models of emotion). While this work was not strictly performance analysis since the authors measured elements that correspond to fixed directions from a score (e.g., mode; pitch height), the authors do analyze attack rate, which is related to timing. Specifically, the authors point out that understanding the role of timing is confounded by the fact that it encompasses several distinct musical properties such as tempo and rhythm. Although the authors do not attempt to segregate these phenomena (tempo and rhythm) in their perceptual experiments, it is clear that for a performer, adjusting the tempo (globally or locally) would influence the attack rate, and therefore have an impact on perceived intensity.

The relation between changes in various expressive parameters and their effect on perceived tension ratings has been fairly well studied but with conflicting results. Krumhansl ( 1996 ) found that in an experiment comparing an original performance to versions with flat dynamics, flat tempo, or both, listeners’ continuous tension ratings were not affected, implying that tension was primarily conveyed by the melodic, harmonic, and durational elements central to the composition (rather than the performance). A similar result was reported by Farbood and Upham ( 2013 ) where repetitions of the same verse across a single performance —as well as a harmonic reduction of it— were found to produce strongly correlated tension ratings. However, Gingras et al. ( 2016 ) studied the relation between musical structure, expressive variation, and listeners’ ratings of musical tension, and found that variations in expressive timing were most predictive of listeners’ tension ratings.

It is equally important to empirically test assumptions about the perceptual effects of expressive variation. For instance, some aspects of so-called ‘micro-timing’ variation —defined as small, systematic, intentional deviations in timing— have been debated with regard to their perceptual effects. In particular, micro-timing has been suggested as one of the principle contributors to the perception of ‘groove’ ( Iyer, 2002 ; Roholt, 2014 ). In fact, there is a sizable portion of literature dedicated to this phenomenon, and the role of micro-timing in generating embodied cognitive responses ( Dibben, 2014 ). However, Davies et al. ( 2013 ) parametrically varied the amount of micro-timing in certain jazz, funk, and samba rhythm patterns, and, contrary to popular belief, found that systematic micro-timing generally led to decreased ratings of perceived groove, naturalness, and liking. Similarly, Frühauf et al. ( 2013 ) found that the highest ratings of perceived ‘groove quality’ were given to drum patterns that were perfectly quantized, and that increasing systematic micro-timing (by shifting either forwards or backwards), resulted in lower quality ratings.

While the role of expressive variation in timbre and intonation has generally been less studied, there has been substantial attention given to the expressive qualities of the singing voice, where these parameters are especially relevant (see Sundberg, 2018 ). For instance, Sundberg et al. ( 2013 ), found that a sharpened intonation at a phrase climax contributed to increased perception of expressiveness and excitement, and Siegwart and Scherer ( 1995 ) found that listener preferences were correlated with certain spectral components such as the relative strength of the fundamental and the value of the spectral centroid. Similarly, the role of ornamentation in contributing to perceived expression, skill, or overall quality, has been largely overlooked, especially as it relates to music outside of the classical canon. Some exceptions include research showing subjective preferences for an idealized pitch contour and timing profile of the Indian classical music ornament Gamak ( Gupta and Rao, 2012 ), and, in pop music, the expressive and emotional effects of portamento (or pitch ‘slides’), as well as the so-called ‘noisy’ sounds of the voice, have been theorized to be of strong importance in generating an emotional response ( Dibben, 2014 ). In the latter case, no actual perceptual experiments have been conducted to investigate this claim, however, it is consistent with ethological research on the role of vocalizations and sub-vocalizations in affective communication ( Huron, 2015 ).

The reason why expressive variation is so enjoyable for listeners remains largely an open research question. Expressive variation is assumed to be the most important cue to a listener that they are hearing a uniquely human performance and is regularly hailed as the key component in communicating an aesthetically pleasing performance. As mentioned above, its role appears to go beyond bolstering the communication of musical structure. And, as pointed out by Repp, even a computerized or metronomic performance will contain grouping cues ( Repp, 1998b ). However, one prominent theory suggests that systematic performance deviations (such as tempo) may generate aesthetically pleasing expressive performances in part due to their exhibiting characteristics that mimic natural motion in the physical world ( Gjerdingen, 1988 ; Todd, 1992 ; Repp, 1993 ; Todd, 1995 ; van Noorden and Moelants, 1999 ) or human movements or gestures ( Ohriner, 2012 ; Broze III, 2013 ). For instance, Friberg and Sundberg ( 1999 ), suggested that the shape of final ritardandi matched the velocity of runners coming to a stop and Juslin ( 2003 ) includes ‘motion principles’ in his model of performance expression.

4.3 Mapping and Predicting Listener Judgments

In order to isolate listeners’ perception of parameters that are strictly performance-related, several scholars have investigated listeners’ judgments across multiple performances of the same excerpt of music (e.g., Repp, 1990 ; Fabian and Schubert, 2008 ). A less-common technique relies on synthesized constructions or manipulations of performances, typically using some kind of rule-based system to manipulate certain musical parameters (e.g., Repp, 1989 ; Sundberg, 1993 ; Clarke, 1993 ; Repp, 1998b ), and frequently making use of continuous data collection measures (e.g., Schubert and Fabian, 2014 ).

From these studies, it appears that listeners (especially ‘trained’ listeners) are capable not only of identifying performance characteristics such as phrasing, articulation, and vibrato, but that they are frequently able to identify them in a manner that is aligned with the performer’s intentions (e.g., Nakamura, 1987 ; Fabian and Schubert, 2009 ). However, while listeners may be able to identify performers’ intentions, they may not have the perceptual acuity to identify certain features with the same precision allowed by acoustic measures. For instance, a study by Howes et al. ( 2004 ) showed there was no correlation between measured and perceived vibrato onset times. Similarly, Geringer ( 1995 ) found that listeners consistently identified increases in intensity (crescendos) with a greater perceived magnitude of contrast than the decreases in intensity (decrescendos) regardless of the actual magnitude of change. This suggests that there are some measurable performance parameters that may not map well to human perception. For example, an objectively measurable difference between a ‘deadpan’ and ‘expressive’ performance does not necessarily translate to perceived expressivity , especially if the changes in measured performance parameters are structurally normative, as discussed in Section 4.2. Two related papers, by Li et al. ( 2015 ) and Sulem et al. ( 2019 ), describe research attempting to better understand the communication chain from score interpretation to performance and performance to perception, respectively. The former attempted to match quantitative acoustic measures with expressive musical terms (commonly used in score directions as the principal means of communicating expressive instruction), while the latter asked performers to match the same expressive musical terms in terms of their perceived emotion along a common dimensional model of emotion (i.e., Russell, 1980 ). This work lays the foundation for future research to empirically examine the full chain of communication; in attempting to manipulate the same acoustic measurements it may be possible to predict perceived musical and emotional correlates.

An important but rarely discussed consideration is the relation between observed differences in a model and the perceptual evaluation of those differences by a listener. For instance, Dixon et al. ( 2006 ) experimented with various methods for extracting perceived tempo information in relation to expressively-performed excerpts with an emphasis on some of the assumptions of beat-tracking algorithms. They discuss the presumption that what is desirable in a beat-tracking model is typically to accurately mark what was performed rather than what was perceived, even though the two may differ. In particular, they note that the perceived beat is smoother than the performance data would indicate. Busse ( 2002 ) evaluated expert listener judgments of the optimal ‘swing style’ of performed jazz piano melodies that were either unmodified, or modified according to one of four ‘derived’ models. The first derived model altered parameters (durations, onsets, and velocities) according to performer averages, whereas the other three derived ‘mechanical’ models had the same parameters fixed by simple ratio relationships. Unsurprisingly, the unaltered and derived models were generally preferred to the mechanical models. (It is well known that some randomness is required in order for a performance to sound convincingly human, and various jitter functions have been implemented in computer music software for this reason since the 1980s.) Despite that one might predict human preference for one of the original (performed) melodies, several of the derived models were not rated statistically different from the original melodies, suggesting that the averaged parameter values created a realistic model. However, the parameters of the unmodified originals were not reported nor compared against each other or those of the derived models, making it impossible to examine any difference thresholds across the measured parameters in terms of their impact on the swing ratings. Devaney ( 2016 ) also compared model classification against human classification using a singer-identification task to explore differences between inter-singer variability and intra-singer similarity across different performance parameters. In general, listeners performed the singer identification task better than chance but far below the abilities of the computational model. However, there were some similarities between the model parameters and the features reported by listeners as important determinants of their classification (e.g., vibrato, pitch stability, timbre, breathiness, intonation). Furthermore, the same pair of singers that ‘confused’ the model were the same two conflated by listeners. These experiments represent excellent examples from a scarce pool of research attempting to bridge MIR and cognitive approaches to performance research. However, only a comparison of systematic manipulations between contrived stimuli will allow sufficient control over the individual parameters necessary to come to definitive conclusions about the perceptibility of variations in performance and their aesthetic value for performance.

Given a weak relation between a measured parameter and listeners’ perception of that parameter, another important question arises: is the parameter itself not useful in modeling human perception, or is the metric simply inappropriate? For example, there are many aspects of music perception that are known to be categorical (e.g., pitch) in which case a continuous metric would not work well in a model designed to predict human ratings.

Similarly, there is the consideration of the role of the representation and transformation of a measured parameter for predicting perceptual ratings. This question was raised by Timmers ( 2005 ), who examined the representation of tempo and dynamics that best predicted listener judgments of musical similarity. This study found that, while most existing models rely on normalized variations of tempo and dynamics, the absolute tempo and the interaction of tempo and loudness were better predictors.

Finally, there are performance features that are either not captured in the audio signal or else not represented in a music performance analysis that may well contribute to a listener’s perception. For instance, if judgments of perception are made in a live setting, then many visual cues —such as performer movement, facial expression, or attire— will be capable of altering the listener’s perception ( Huang and Krumhansl, 2011 ; Juchniewicz, 2008 ; Wapnick et al., 2009 ; Livingstone et al., 2009 ; Silvey, 2012 ). Importantly, visual information such as performer gesture and movement may contribute to embodied sensorimotor engagement, which is thought to be an essential component of music perception (e.g., Leman and Maes, 2014 ; Bishop and Goebl, 2018 ), and could therefore be influential on ratings of performance aesthetics and/or musical expression.

Clearly, the execution of multiple performance parameters is important for the perception of both small-scale and large-scale musical structures, and appears to have a large influence over listeners’ perception and experience of the emotional and expressive aspects of a performance. Since the latter appears to carry great significance for both MPA and music perception research, it suggests that future work ought to focus on disentangling the relative weighting of the various features controlled by performers that contribute to an expressive performance. Since it is frequently alluded to that a performer’s manipulation of musical tension is one of the strongest contributors to an expressive performance, further empirical research must attempt to systematically break down the concept of tension as a high-level feature into meaningful collections of smaller, well-defined features that would be useful for MPA.

4.4 Challenges

The research surveyed in this section highlights the importance of human perception in MPA research, especially as it pertains to the communication of emotion, musical structure, and creating an aesthetically pleasing performance. In fact, the successful modeling of perceptually relevant performance attributes, such as those that mark ‘expressiveness’, could have a large impact not only for MPA but for many other areas of MIR research, such as computer-generated performance, automatic accompaniment, virtual instrument design and control, or robotic instruments and HCI (see, for example, the range of topics discussed by Kirke and Miranda ( 2013 )). A major obstacle impeding research in this area is the inability to successfully isolate (and therefore understand) the various performance characteristics that contribute to a so-called ‘expressive’ performance from a listener’s perspective. Existing literature reviews on the topic of MPA have not been able to shed much light on this problem, in part because researchers frequently disagree on (or conflate) the various definitions of ‘expressive,’ or else findings appear inconsistent across the research, likely as a result of different methodologies, types of comparisons, or data. As noted by Devaney ( 2016 ), combining computational and listening experiments could lead to a better understanding of which aspects of variation are important to observe and model. Careful experimental design and/or meta-analyses across both MPA and cognition research, as well as cross-collaboration between MIR and music cognition researchers, may therefore prove fruitful endeavors for future research.

5. Performance Assessment

Assessment of musical performances deals with providing a rating of a music performance with regard to specific aspects of the performance such as accuracy, expressivity, and virtuosity. Performance assessment is a critical and ubiquitous aspect of music pedagogy: students rely on regular feedback from teachers to learn and improve skills, recitals are used to monitor progress, and selection into ensembles is managed through competitive auditions. The performance parameters on which these assessments are based are not only subjective but also ill-defined, leading to large differences in subjective opinion among music educators ( Thompson and Williamon, 2003 ; Wesolowski et al., 2016 ). However, other studies have shown that humans tend to rate prototypical (average) performances higher than individual performances ( Repp, 1997a ; Wolf et al., 2018 ). This might indicate that performances are rated based on some form of perceived distance from an ‘ideal’ performance. Apart from music education, assessment of performances is also an important area of focus for the evaluation of computer-generated music performances ( Bresin and Friberg, 2013 ) where researchers have primarily focused on listening studies to understand the effect of musical knowledge and biases on rating performances ( De Poli et al., 2014 ) and the degree to which computer generated performances stack up against those by humans ( Schubert et al., 2017 ).

Work within assessment-focused MPA deals with modeling how humans assess a musical performance. The goal is to increase the objectivity of performance assessments ( McPherson and Thompson, 1998 ) and to build accessible and reliable tools for automatic assessment. While this might be considered a subset of listener-focused MPA, its importance to MPA research and music education warrants a tailored review of research in this area.

Over the last decade, several researchers have worked towards developing tools capable of automatic music performance assessment. These can be loosely categorized based on (i) the parameters of the performance that are assessed, and (ii) the technique/method used to design these systems.

5.1 Assessment parameters

Tools for performance assessment evaluate one or more performance parameters typically related to the accuracy of the performance in terms of pitch and timing ( Wu et al., 2016 ; Vidwans et al., 2017 ; Pati et al., 2018 ; Luo, 2015 ), or quality of sound (timbre) ( Knight et al., 2011 ; Romani Picas et al., 2015 ; Narang and Rao, 2017 ). In building an assessment tool, the choice of parameters may depend on the proficiency level of the performer being assessed. For example, beginners will benefit more from feedback in terms of low-level parameters such as pitch or rhythmic accuracy as opposed to feedback on higher-level parameters such as articulation or expression. Assessment parameters can also be specific to culture or the musical style under consideration, for example, in the case of Indian classical music the nature of pitch transitions or gamakas plays an important role ( Gupta and Rao, 2012 ), while correct pronunciation of syllables is a strict requirement for Chinese Jingju music ( Gong, 2018 ).

Assessment tools can also vary based on the granularity of assessments. Tools may simply classify a performance as ‘good’ or ‘bad’ ( Knight et al., 2011 ; Nakano et al., 2006 ), or grade it on a scale, e.g., from 1 to 10 ( Pati et al., 2018 ). Systems may provide fine-grained note-by-note assessments ( Romani Picas et al., 2015 ; Schramm et al., 2015 ) or analyze entire performances and report a single assessment score ( Nakano et al., 2006 ; Pati et al., 2018 ; Huang and Lerch, 2019 ).

5.2 Assessment methods

While different methods have been used to create performance assessment tools, the common approach has been to use descriptive features extracted from the audio recording of a performance, based on which a classifier predicts the assessment. This approach requires availability of performance data (recordings) along with human (expert) assessments for the rated performance parameters.

The level of sophistication of classifiers was limited especially for early attempts, in which classifiers such as Support Vector Machines were used to predict human ratings. In these systems, the descriptive features became an important aspect of the system design. In some approaches, standard spectral and temporal features such as spectral centroid, spectral flux, and zero-crossing rate were used ( Knight et al., 2011 ). In others, features aimed at capturing certain aspects of music perception were hand-designed using either musical intuition or expert knowledge ( Nakano et al., 2006 ; Abeßer et al., 2014b ; Romani Picas et al., 2015 ; Li et al., 2015 ). For instance, Nakano et al. ( 2006 ) used features measuring pitch stability and vibrato as inputs to a simple classifier to rate the quality of vocal performances. Several studies also attempted to combine low-level audio features with hand-designed feature sets ( Luo, 2015 ; Wu et al., 2016 ; Vidwans et al., 2017 ), as well as incorporating information from the musical score or reference performance recordings into the feature computation process ( Devaney et al., 2012 ; Mayor et al., 2009 ; Vidwans et al., 2017 ; Bozkurt et al., 2017 ; Molina et al., 2013 ; Falcao et al., 2019 ).

Recent methods, however, have transitioned towards using advanced machine learning techniques such as sparse coding ( Han and Lee, 2014 ; Wu and Lerch, 2018c , a ) and deep learning ( Pati et al., 2018 ). Contrary to earlier methods which focused on hand-designing musically important features, these techniques input raw data (usually in the form of pitch contours or spectrograms) and train the models to automatically learn meaningful features so as to accurately predict the assessment ratings.

In some ways, this evolution in methodology has mirrored that of other MIR tasks: there has been a gradual transition from feature design to feature learning (compare Figure 3 ). Feature design and feature learning have an inherent trade-off. Learned features extract relevant information from data which might not be represented in the hand-crafted feature set. This is evident from their superior performance at assessment modeling tasks ( Wu and Lerch, 2018a ; Pati et al., 2018 ). However, this superior performance comes at the cost of low interpretability. Learned features tend to be abstract and cannot be easily understood. Custom-designed features, on the other hand, typically either measure a simple low-level characteristic of the audio signal or link to high-level semantic concepts such as pitch or rhythm which are intuitively interpretable. Thus, such models allow analysis that can aid in the interpretation of semantic concepts for music performance assessment. For instance, Gururani et al. ( 2018 ) analyzed the impact of different features on an assessment prediction task and found that features measuring tempo variations were particularly critical, and that score-aligned features performed better than score-independent features.

Schematic showing the comparison between different approaches for music performance assessment.

5.3 Challenges

In spite of several attempts across varied performance parameters using different methods, the important features for assessing music performances remain unclear. This is evident from the average accuracy of these tools in modeling human judgments. Most of the presented models either work well only for very select data ( Knight et al., 2011 ) or have comparably low prediction accuracies ( Vidwans et al., 2017 ; Wu et al., 2016 ), rendering them unusable in most practical scenarios ( Eremenko et al., 2020 ). While this may be partially attributed to the subjective nature of the task itself, there are several other factors which have limited the improvement of these tools. First, most of the models are trained on small task-specific or instrument-specific datasets that might not reflect noisy real-world data. This reduces the generalizability of these models. The problem becomes more serious for data-hungry methods such as deep learning which require large amounts of data for training. The larger datasets (>3000 performances) based on real-world data are either not publicly available (for example, the FBA dataset ( Pati et al., 2018 )) or only provide intermediate representations such as pitch contours (for example, the MAST melody dataset ( Bozkurt et al., 2017 )). Thus, more efforts are needed towards creating and releasing larger performance datasets for the research community. Second, the distribution of ground-truth (expert) ratings given by human judges, in many datasets, skewed towards a particular class or value ( Gururani et al., 2018 ). This makes it challenging to train unbiased models. Finally, the performance parameters required to adequately model a performance are not well understood. While the typical approach is to train different models for different parameters, this approach necessitates availability of performance data along with expert assessments for all these parameters. On many occasions, such assessments are either not available or are costly to obtain. For instance, while the MAST rhythm dataset ( Falcao et al., 2019 ) contains performance recordings (and pass/fail assessment ratings) for around 1000 students, the finely annotated (on a 4-point scale) version of the same dataset contains only 80 performances. An interesting direction for future research might consider leveraging models which are successful at assessing a few parameters (and/or instruments) to improve the performance of models for other parameters (and/or instruments). This approach, usually referred to as transfer learning, has been found to be successful in other MIR tasks ( Choi et al., 2017 ).

In addition to the data-related challenges, there are several other challenging problems for MIR researchers interested in this domain. Better techniques need to be developed to factor the score (or reference) information into the assessments. So far, this has been accomplished by either using dynamic time warping (DTW) based methods ( Vidwans et al., 2017 ; Bozkurt et al., 2017 ; Molina et al., 2013 ) to compute distance-based features between the reference and the performance or by computing vector similarity between features extracted from the performance and the reference ( Falcao et al., 2019 ). However, expressive performances are supposed to deviate from the score and simple distance-based features may fail to adequately capture the nuances. The problem of how to incorporate this information into the assessment computation process remains an open problem.

Another area which requires attention from researchers lies in improving the ability to interpret and understand the features learned by end-to-end models. This will play an important role in improving assessment tools. Interpretability of neural networks is still an active area of research, and performance assessment is an excellent testbed for developing such methods.

6. Conclusion

The previous sections outlined insights gained by MPA at the intersection of audio content analysis, empirical musicology, and music perception research. These insights are of importance for better understanding the process of making music as well as affective user reactions to music.

6.1 Applications

The better understanding of music performance enables a considerable range of applications spanning a multitude of different areas including systematic musicology, music education, MIR, and computational creativity, leading to a new generation of music discovery and recommendation systems, and generative music systems.

The most obvious application example connecting MPA and MIR is music tutoring software. Such software aims at supplementing teachers by providing students with insights and interactive feedback by analyzing and assessing the audio of practice sessions. The ultimate goals of an interactive music tutor are to highlight problematic parts of the student’s performance, provide a concise yet easily understandable analysis, give specific and understandable feedback on how to improve, and individualize the curriculum depending on the student’s mistakes and general progress. Various (commercial) solutions are already available, exhibiting a similar set of goals. These systems adopt different approaches, ranging from traditional music classroom settings to games targeting a playful learning experience. Examples for tutoring applications are SmartMusic, 1 Yousician, 2 Music Prodigy, 3 and SingStar. 4 However, many of these tools are not reliable enough to be used in educational settings. More studies are needed to properly evaluate the usability of performance assessment systems in real classroom environments ( Eremenko et al., 2020 ).

Performance parameters have a long history being either explicitly or implicitly part of MIR systems. For instance, core MIR tasks such as music genre classification and music recommendation systems have a long history of utilizing tempo and dynamics features successfully ( Fu et al., 2011 ).

Another area which has relied extensively on using performance data is the field of generative modeling. Much of the recent research has been on generating expressive performances with or without a musical score as input. While the vast majority of this body of work has focused on piano performances ( Cancino-Chacón and Grachten, 2016 ; Malik and Ek, 2017 ; Jeong et al., 2019 ; Jeong et al., 2019b , a ; Oore et al,. 2020 ; Maezawa et al., 2019 ), there are a few studies focused on other instruments such as violin and flute ( Wang and Yang, 2019 ). The common thread across these approaches is that they use end-to-end data-driven techniques to generate the performance (either predict note-wise performance features such as timing, tempo and dynamics, or directly generate the audio) given the score as input. While these methods have achieved some success, they mostly operate as black boxes, and hence, lack in their ability to either provide deeper insights regarding the performance generation process or exert any form of explicit control over different performance parameters. There have been some attempts to alleviate these limitations. For instance, Maezawa et al. ( 2019 ) tried to learn an abstract representation capturing the musical interpretation of the performer. This could allow generation of different performances of the same piece with varying interpretations. More studies like this would allow better modeling of musical performances and improving the quality and usability of performance generation systems.

6.2 Challenges

Despite such practical applications, there are still many open topics and challenges that need to be addressed. The main challenges of MPA have been summarized at the end of each of the previous sections. The related challenges to the MIR community, however, are multi-faceted as well. First, the fact that the majority of the presented studies use manual annotations instead of automated methods should encourage the MIR community to re-evaluate the measures of success of their proposed systems if, as it appears to be, the outputs lack the robustness or accuracy required for a detailed analysis even for tasks considered to be ‘solved.’ Second, the missing separation of composition and performance parameters when framing research questions or problem definitions can impact not only interpretability and reusability of insights but might also reduce algorithm performance. If, for example, a music emotion recognition system does not differentiate between the impact of core musical ideas and performance characteristics, it will have a harder time differentiating relevant and irrelevant information. Thus, it is essential for MIR systems to not only differentiate between score and performance parameters in the system design phase but also analyze their respective contributions during evaluation. Third, when examining phenomena that are complex and at times ambiguous —such as ‘expressiveness’— it is imperative to fully define the scope of the associated terminology. Inconsistently used or poorly defined terms can obfuscate results making it more challenging to build on prior work or to propagate knowledge across disciplines. Fourth, a greater flow of communication between MIR and music perception communities would bolster research in both areas. However, differing methodologies, tools, terminology, and approaches have often created a barrier to such an exchange ( Aucouturier and Bigand, 2012 ). One way of facilitating this communication between disciplines is to maximize the interpretability and reusability of results. In particular, acknowledging or addressing the perceptual relevance of predictor variables or results, or even explicitly pointing to a possible gap in the perceptual literature, can aid knowledge transfer by pointing to ‘meaningful’ or perceptually-relevant features to focus subsequent empirical work. In addition, it would be prudent to ensure that any underlying assumptions of perceptual validity (linked to methods or results) are made overt and, where possible, supported with empirical results. Fifth, lack of data continues to be a challenge for both MIR core tasks and MPA; a focus on approaches for limited data ( McFee et al., 2015 ), weakly labeled data, and unlabeled data ( Wu and Lerch 2018b ) could help address this problem. There is, however, a slow but steady growth in the number of datasets available for performance analysis, indicating growing awareness and interest in this topic. Table 1 lists the most relevant currently available datasets for music performance research. Note that 22 of the 30 datasets listed have been released in the last 5 years.

In conclusion, the fields of MIR and MPA each depend on the advances in the other field. In addition, music perception and cognition, while not a traditional topic within MIR, can be seen as an important linchpin for the advancement of MIR systems that depend on reliable and diverse perceptual data. Cross-disciplinary approaches to MPA bridging methodologies and data from music cognition and MIR are likely to be most influential for future research. Empirical, descriptive research driven by advanced audio analysis is necessary to extend our understanding of music and its perception, which in turn will allow us to create better systems for music analysis, music understanding, and music creation.

MakeMusic, Inc., www.smartmusic.com , last accessed 04/11/2019.

Yousician Oy, www.yousician.com , last accessed 04/11/2019.

The Way of H, Inc., www.musicprodigy.com , last accessed 04/11/2019.

Sony Interactive Entertainment Europe, www.singstar.com , last accessed 04/11/2019.

Competing Interests

The authors have no competing interests to declare.

Abeßer, J., Cano, E., Frieler, K., & Pfleiderer, M. (2014a). Dynamics in jazz improvisation: Scoreinformed estimation and contextual analysis of tone intensities in trumpet and saxophone solos. In 9th Conference on Interdisciplinary Musicology (CIM14), Berlin, Germany.

Abeßer, J., Cano, E., Frieler, K., & Zaddach, W.-G. (2015). Score-informed analysis of intonation and pitch modulation in jazz solos. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Malaga, Spain.

Abeßer, J., Frieler, K., Cano, E., Pfleiderer, M., & Zaddach, W.-G. (2017). Score-informed analysis of tuning, intonation, pitch modulation, and dynamics in jazz solos. IEEE Transactions on Audio, Speech and Language Processing , 25(1), 168–177. DOI: https://doi.org/10.1109/TASLP.2016.2627186

Abeßer, J., Hasselhorn, J., Grollmisch, S., Dittmar, C., & Lehmann, A. (2014b). Automatic competency assessment of rhythm performances of ninth-grade and tenth-grade pupils. In Proceedings of the International Computer Music Conference (ICMC), Athens, Greece.

Abeßer, J., Lukashevich, H., & Schuller, G. (2010). Feature-based extraction of plucking and expression styles of the electric bass guitar. In Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 2290–2293, Dallas. DOI: https://doi.org/10.1109/ICASSP.2010.5495945

Abeßer, J., Pfleiderer, M., Frieler, K., & Zaddach, W.-G. (2014c). Score-informed tracking and contextual analysis of fundamental frequency contours in trumpet and saxophone jazz solos. In Proceedings of the International Conference on Digital Audio Effects (DAFx), Erlangen, Germany.

Arzt, A., & Widmer, G. (2015). Real-time music tracking using multiple performances as a reference. In Müller, M., & Wiering, F., editors, Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 357–363, Malaga, Spain.

Ashley, R. (2002). Do[n’t] change a hair for me: The art of jazz rubato. Music Perception: An Interdisciplinary Journal , 19(3), 311–332. DOI: https://doi.org/10.1525/mp.2002.19.3.311

Atli, H. S., Bozkurt, B., & Sentürk, S. (2015). A method for tonic frequency identification of Turkish makam music recordings. In Proceedings of the 5th International Workshop on Folk Music Analysis (FMA), Paris, France. Association Dirac. DOI: https://doi.org/10.1109/SIU.2015.7130148

Aucouturier, J.-J., & Bigand, E. (2012). Mel Cepstrum & Ann Ova: The difficult dialog between MIR and music cognition. In ISMIR , pages 397–402.

Bantula, H., Giraldo, S. I., & Ramirez, R. (2016). Jazz ensemble expressive performance modeling. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), New York.

Battcock, A., & Schutz, M. (2019). Acoustically expressing affect. Music Perception , 37(1), 66–91. DOI: https://doi.org/10.1525/mp.2019.37.1.66

Bayle, Y., Maršík, L., Rusek, M., Robine, M., Hanna, P., Slaninová, K., Martinovic, J., & Pokorný, J. (2017). Kara1k: A karaoke dataset for cover song identification and singing voice analysis. In International Symposium on Multimedia (ISM), pages 177–184, Taichung, Taiwan. IEEE. DOI: https://doi.org/10.1109/ISM.2017.32

Behne, K.-E., & Wetekam, B. (1993). Musikpsychologische Interpretationsforschung: Individualität und Intention. Musikpsychologie. Jahrbuch der Deutschen Gesellschaft für Musikpsychologie , 10, 24–37.

Bektaş, T. (2005). Relationships between prosodic and musical meters in the beste form of classical Turkish music. Asian Music , 36(1), 1–26. DOI: https://doi.org/10.1353/amu.2005.0003

Benetos, E., Dixon, S., Giannoulis, D., Kirchhoff, H., & Klapuri, A. (2013). Automatic music transcription: Challenges and future directions. Journal of Intelligent Information Systems , 41(3), 407–434. DOI: https://doi.org/10.1007/s10844-013-0258-3

Bergeron, V., & Lopes, D. M. (2009). Hearing and seeing musical expression. Philosophy and Phenomenological Research , 78(1), 1–16. DOI: https://doi.org/10.1111/j.1933-1592.2008.00230.x

Bishop, L., & Goebl, W. (2018). Performers and an active audience: Movement in music production and perception. Jahrbuch Musikpsychologie , 28, 1–17. DOI: https://doi.org/10.5964/jbdgm.2018v28.19

Bowman Macleod, R. (2006). Influences of Dynamic Level and Pitch Height on the Vibrato Rates and Widths of Violin and Viola Players . Dissertation, Florida State University, College of Music, Tallahassee, FL.

Bozkurt, B., Ayangil, R., & Holzapfel, A. (2014). Computational analysis of Turkish makam music: Review of state-of-the-art and challenges. Journal of New Music Research , 43(1), 3–23. DOI: https://doi.org/10.1080/09298215.2013.865760

Bozkurt, B., Baysal, O., & Yüret, D. (2017). A dataset and baseline system for singing voice assessment. In Proceedings of the International Symposium on Computer Music Modeling and Retrieval (CMMR), Matosinhos.

Bresin, R., & Friberg, A. (2013). Evaluation of computer systems for expressive music performance. In Kirke, A., & Miranda, E. R., editors, Guide to Computing for Expressive Music Performance , pages 181–203. Springer, London. DOI: https://doi.org/10.1007/978-1-4471-4123-5_7

Broze, G. J. III (2013). Animacy, Anthropomimesis, and Musical Line . PhD Thesis, The Ohio State University.

Busse, W. G. (2002). Toward objective measurement and evaluation of jazz piano performance via MIDIbased groove quantize templates. Music Perception: An Interdisciplinary Journal , 19(3), 443–461. DOI: https://doi.org/10.1525/mp.2002.19.3.443

Cancino-Chacón, C. E., Gadermaier, T., Widmer, G., & Grachten, M. (2017). An evaluation of linear and non-linear models of expressive dynamics in classical piano and symphonic music. Machine Learning , 106(6), 887–909. DOI: https://doi.org/10.1007/s10994-017-5631-y

Cancino-Chacón, C. E., & Grachten, M. (2016). The Basis Mixer: A computational Romantic pianist. In Late Breaking Demo (Extended Abstract), International Society for Music Information Retrieval Conference (ISMIR), New York.

Cancino-Chacón, C. E., Grachten, M., Goebl, W., & Widmer, G. (2018). Computational models of expressive music performance: A comprehensive and critical review. Frontiers in Digital Humanities , 5. DOI: https://doi.org/10.3389/fdigh.2018.00025

Caro Repetto, R., Gong, R., Kroher, N., & Serra, X. (2015). Comparision of the singing style of two Jingju schools. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Malaga, Spain.

Chen, K. (2013). Characterization of pitch intonation of Beijing opera. Master’s thesis, Universitat Pompeu Fabra, Barcelona.

Cheng, E., & Chew, E. (2008). Quantitative analysis of phrasing strategies in expressive performance: Computational methods and analysis of performances of unaccompanied Bach for solo violin. Journal of New Music Research , 37(4), 325–338. DOI: https://doi.org/10.1080/09298210802711660

Chew, E. (2016). Playing with the edge: Tipping points and the role of tonality. Music Perception , 33(3), 344–366. DOI: https://doi.org/10.1525/mp.2016.33.3.344

Choi, K., Fazekas, G., Sandler, M. B., & Cho, K. (2017). Transfer learning for music classification and regression tasks. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 141–149, Suzhou, China.

Chuan, C.-H., & Chew, E. (2007). A dynamic programming approach to the extraction of phrase boundaries from tempo variations in expressive performances. In Proceedings of the International Conference on Music Information Retrieval (ISMIR), Vienna, Austria.

Clarke, E. F. (1993). Imitating and evaluating real and transformed musical performances. Music Perception: An Interdisciplinary Journal , 10(3), 317–341. DOI: https://doi.org/10.2307/40285573

Clarke, E. F. (1998). Rhythm and timing in music. In The Psychology of Music , pages 473–500. Academic Press, San Diego, 2nd edition. DOI: https://doi.org/10.1016/B978-012213564-4/50014-7

Clarke, E. F. (2002a). Listening to performance. In Rink, J., editor, Musical Performance – A Guide to Understanding . Cambridge University Press, Cambridge. DOI: https://doi.org/10.1017/CBO9780511811739.014

Clarke, E. F. (2002b). Understanding the psychology of performance. In Rink, J., editor, Musical Performance – A Guide to Understanding . Cambridge University Press, Cambridge. DOI: https://doi.org/10.1017/CBO9780511811739.005

Clayton, M. (2008). Time in Indian Music: Rhythm, Metre, and Form in North Indian Rag Performance . Oxford University Press, New York.

Collier, G. L., & Collier, J. L. (1994). An exploration of the use of tempo in jazz. Music Perception: An Interdisciplinary Journal , 11(3), 219–242. DOI: https://doi.org/10.2307/40285621

Collier, G. L., & Collier, J. L. (2002). A study of timing in two Louis Armstrong solos. Music Perception: An Interdisciplinary Journal , 19(3), 463–483. DOI: https://doi.org/10.1525/mp.2002.19.3.463

Cuesta, H., Gómez Gutiérrez, E., Martorell Domínguez, A., & Loáiciga, F. (2018). Analysis of intonation in unison choir singing. In Proceedings of the International Conference on Music Perception and Cognition (ICMPC), Graz, Austria.

Dalla Bella, S., & Palmer, C. (2004). Tempo and dynamics in piano performance: The role of movement amplitude. In Proceedings of the 8th International Conference on Music Perception & Cognition (ICMPC), Evanston.

Davies, M., Madison, G., Silva, P., & Gouyon, F. (2013). The effect of microtiming deviations on the perception of groove in short rhythms. Music Perception: An Interdisciplinary Journal , 30(5), 497–510. DOI: https://doi.org/10.1525/mp.2013.30.5.497

De Poli, G., Canazza, S., Rodà, A., & Schubert, E. (2014). The role of individual difference in judging expressiveness of computer-assisted music performances by experts. ACM Transactions on Applied Perception , 11(4), 22:1–22:20. DOI: https://doi.org/10.1145/2668124

Dean, R. T., Bailes, F., & Drummond, J. (2014). Generative structures in improvisation: Computational segmentation of keyboard performances. Journal of New Music Research , 43(2), 224–236. DOI: https://doi.org/10.1080/09298215.2013.859710

Devaney, J. (2016). Inter- versus intra-singer similarity and variation in vocal performances. Journal of New Music Research , 45(3), 252–264. DOI: https://doi.org/10.1080/09298215.2016.1205631

Devaney, J., Mandel, M. I., Ellis, D. P. W., & Fujinaga, I. (2011). Automatically extracting performance data from recordings of trained singers. Psychomusicology: Music, Mind and Brain , 21(1–2), 108–136. DOI: https://doi.org/10.1037/h0094008

Devaney, J., Mandel, M. I., & Fujinaga, I. (2012). A study of intonation in three-part singing using the Automatic Music Performance Analysis and Comparison Toolkit (AMPACT). In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal.

Dibben, N. (2014). Understanding performance expression in popular music recordings. In Fabian, D., Timmers, R., & Schubert, E., editors, Expressiveness in Music Performance: Empirical Approaches Across Styles and Cultures . Oxford University Press. DOI: https://doi.org/10.1093/acprof:oso/9780199659647.003.0007

Dillon, R. (2001). Extracting audio cues in real time to understand musical expressiveness. In Proceedings of the MOSART workshop, Barcelona.

Dillon, R. (2003). A statistical approach to expressive intention recognition in violin performances. In Proceedings of the Stockholm Music Acoustics Conference (SMAC), Stockholm.

Dillon, R. (2004). On the Recognition of Expressive Intentions in Music Playing: A Computational Approach with Experiments and Applications . Dissertation, University of Genoa, Faculty of Engineering, Genoa.

Dimov, T. (2010). Short Historical Overview and Comparison of the Pitch Width and Speed Rates of the Vibrato Used in Sonatas and Partitas for Solo Violin by Johann Sebastian Bach as Found in Recordings of Famous Violinists of the Twentieth and the Twenty- First Centuries . D.M.A., West Virginia University, United States.

Dittmar, C., Pfleiderer, M., Balke, S., & Müller, M. (2018). A swingogram representation for tracking micro-rhythmic variation in jazz performances. Journal of New Music Research , 47(2), 97–113. DOI: https://doi.org/10.1080/09298215.2017.1367405

Dixon, S., Goebl, W., & Cambouropoulos, E. (2006). Perceptual smoothness of tempo in expressively performed music. Music Perception , 23(3), 195–214. DOI: https://doi.org/10.1525/mp.2006.23.3.195

Dixon, S., Goebl, W., & Widmer, G. (2002). The Performance Worm: Real time visualisation of expression based on Langner’s tempo loudness animation. In Proceedings of the International Computer Music Conference (ICMC), Göteborg.

Ellis, M. C. (1991). An analysis of “swing” subdivision and asynchronization in three jazz saxophonists. Perceptual and Motor Skills , 73(3), 707–713. DOI: https://doi.org/10.2466/pms.1991.73.3.707

Eremenko, V., Morsi, A., Narang, J., & Serra, X. (2020). Performance assessment technologies for the support of musical instrument learning. In Proceedings of the International Conference on Computer Supported Education (CSEDU), Prague. DOI: https://doi.org/10.5220/0009817006290640

Fabian, D., & Schubert, E. (2008). Musical character and the performance and perception of dotting, articulation and tempo in 34 recordings of Variation 7 from J.S. Bach’s Goldberg Variations (BWV 988). Musicae Scientiae , 12(2), 177–206. DOI: https://doi.org/10.1177/102986490801200201

Fabian, D., & Schubert, E. (2009). Baroque expressiveness and stylishness in three recordings of the D minor Sarabanda for solo violin (BWV 1004) by J. S. Bach. Music Performance Research , 3, 36–55.

Falcao, F., Bozkurt, B., Serra, X., Andrade, N., & Baysal, O. (2019). A dataset of rhythmic pattern reproductions and baseline automatic assessment system. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Delft, The Netherlands.

Farbood, M. M., & Upham, F. (2013). Interpreting expressive performance through listener judgments of musical tension. Frontiers in Psychology , 4. DOI: https://doi.org/10.3389/fpsyg.2013.00998

Finney, S. A., & Palmer, C. (2003). Auditory feedback and memory for music performance: Some evidence for an encoding effect. Memory & Cognition , 31(1), 51–64. DOI: https://doi.org/10.3758/BF03196082

Franz, D. M. (1998). Markov chains as tools for jazz improvisation analysis. Master’s thesis, Virginia Tech.

Friberg, A., & Sundberg, J. (1999). Does music performance allude to locomotion? A model of final ritardandi derived from measurements of stopping runners. The Journal of the Acoustical Society of America , 105(3), 1469–1484. DOI: https://doi.org/10.1121/1.426687

Friberg, A., & Sundström, A. (2002). Swing ratios and ensemble timing in jazz performance: Evidence for a common rhythmic pattern. Music Perception: An Interdisciplinary Journal , 19(3), 333–349. DOI: https://doi.org/10.1525/mp.2002.19.3.333

Frieler, K., Pfleiderer, M., Zaddach, W.-G., & Abeßer, J. (2016). Midlevel analysis of monophonic jazz solos: A new approach to the study of improvisation. Musicae Scientiae , 20(2), 143–162. DOI: https://doi.org/10.1177/1029864916636440

Frühauf, J., Kopiez, R., & Platz, F. (2013). Music on the timing grid: The influence of microtiming on the perceived groove quality of a simple drum pattern performance. Musicae Scientiae , 17(2), 246–260. DOI: https://doi.org/10.1177/1029864913486793

Fu, Z., Lu, G., Ting, K. M., & Zhang, D. (2011). A survey of audio-based music classification and annotation. IEEE Transactions on Multimedia , 13(2), 303–319. DOI: https://doi.org/10.1109/TMM.2010.2098858

Gabrielsson, A. (1987). Once again: The theme from Mozart’s Piano Sonata in A Major (K. 331): A comparison of five performances. In Gabrielsson, A., editor, Action and Perception in Rhythm and Music , pages 81–103. Royal Swedish Academy of Music, No. 55, Stockholm.

Gabrielsson, A. (1999). The performance of music. In Deutsch, D., editor, The Psychology of Music . Academic Press, San Diego, 2nd edition. DOI: https://doi.org/10.1016/B978-012213564-4/50015-9

Gabrielsson, A. (2003). Music performance research at the millennium. Psychology of Music , 31(3), 221–272. DOI: https://doi.org/10.1177/03057356030313002

Gadermaier, T., & Widmer, G. (2019). A study of annotation and alignment accuracy for performance comparison in complex orchestral music. In Flexer, A., Peeters, G., Urbano, J., & Volk, A., editors, Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 769–775, Delft, Netherlands.

Ganguli, K. K., & Rao, P. (2017). Towards computational modeling of the ungrammatical in a raga performance. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Suzhou, China.

Gasser, M., Arzt, A., Gadermaier, T., Grachten, M., & Widmer, G. (2015). Classical music on the web: User interfaces and data representations. In Müller, M., & Wiering, F., editors, Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 571–577, Malaga, Spain.

Geringer, J. M. (1995). Continuous loudness judgments of dynamics in recorded music excerpts. Journal of Research in Music Education , 43(1), 22–35. DOI: https://doi.org/10.2307/3345789

Gillick, J., Roberts, A., Engel, J., Eck, D., & Bamman, D. (2019). Learning to groove with inverse sequence transformations. In Proceedings of the International Conference on Machine Learning (ICML), pages 2269–2279.

Gingras, B., Pearce, M. T., Goodchild, M., Dean, R. T., Wiggins, G., & McAdams, S. (2016). Linking melodic expectation to expressive performance timing and perceived musical tension. Journal of Experimental Psychology: Human Perception and Performance , 42(4), 594. DOI: https://doi.org/10.1037/xhp0000141

Gjerdingen, R. O. (1988). Shape and motion in the microstructure of song. Music Perception: An Interdisciplinary Journal , 6(1), 35–64. DOI: https://doi.org/10.2307/40285415

Goebl, W. (1999). Numerisch-klassifikatorische Interpretationsanalyse mit dem ‘Bösendorfer Computerflügel’ . Diploma Thesis, Universität Wien, Vienna, Austria.

Goebl, W. (2001). Melody lead in piano performance: Expressive device or artifact? The Journal of the Acoustical Society of America , 110(1), 563–572. DOI: https://doi.org/10.1121/1.1376133

Goebl, W., Dixon, S., De Poli, G., Friberg, A., Bresin, R., & Widmer, G. (2005). ‘Sense’ in expressive music performance: Data acquisition, computational studies, and models. In Leman, M., & Cirotteau, D., editors, Sound to Sense, Sense to Sound: A Stateof- the-Art . Logos, Berlin.

Goebl, W., & Palmer, C. (2008). Tactile feedback and timing accuracy in piano performance. Experimental Brain Research , 186(3), 471–479. DOI: https://doi.org/10.1007/s00221-007-1252-1

Gong, R. (2018). Automatic Assessment of Singing Voice Pronunciation: A Case Study with Jingju Music . PhD Thesis, Universitat Pompeu Fabra, Barcelona, Spain.

Gong, R., Yang, Y., & Serra, X. (2016). Pitch contour segmentation for computer-aided Jingju singing training. In Proceedings of the Sound and Music Computing Conference (SMC), Hamburg, Germany.

Grachten, M., Cancino-Chacón, C. E., & Chacón, C. E. C. (2017). Temporal dependencies in the expressive timing of classical piano performances. In The Routledge Companion to Embodied Music Interaction , pages 360–369. Routledge, New York. DOI: https://doi.org/10.4324/9781315621364-40

Grachten, M., & Widmer, G. (2012). Linear basis models for prediction and analysis of musical expression. Journal of New Music Research , 41(4), 311–322. DOI: https://doi.org/10.1080/09298215.2012.731071

Gupta, C., & Rao, P. (2012). Objective assessment of ornamentation in Indian classical singing. In Ystad, S., Aramaki, M., Kronland-Martinet, R., Jensen, K., & Mohanty, S., editors, Speech, Sound and Music Processing: Embracing Research in India, Lecture Notes in Computer Science , pages 1–25. Springer Berlin Heidelberg.

Gururani, S., Pati, K. A., Wu, C.-W., & Lerch, A. (2018). Analysis of objective descriptors for music performance assessment. In Proceedings of the International Conference on Music Perception and Cognition (ICMPC), Toronto, Ontario, Canada.

Hakan, T., Serra, X., & Arcos, J. L. (2012). Characterization of embellishments in ney performances of makam music in Turkey. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal.

Han, Y., & Lee, K. (2014). Hierarchical approach to detect common mistakes of beginner flute players. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 77–82, Taipei, Taiwan.

Hartmann, A. (1932). Untersuchungen über das metrische Verhalten in musikalischen Interpretationsvarianten. Archiv für die gesamte Psychologie , 84, 103–192.

Hashida, M., Matsui, T., & Katayose, H. (2008). A new music database describing deviation information of performance expressions. In Bello, J. P., Chew, E., & Turnbull, D., editors, Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 489–494, Philadelphia, PA.

Hawthorne, C., Stasyuk, A., Roberts, A., Simon, I., Huang, C.-Z. A., Dieleman, S., Elsen, E., Engel, J., & Eck, D. (2019). Enabling factorized piano music modeling and generation with the MAESTRO Dataset. In Proceedings of the International Conference on Learning Representations (ICLR). arXiv: 1810.12247.

Henkel, F., Balke, S., Dorfer, M., & Widmer, G. (2019). Score following as a multi-modal reinforcement learning problem. Transactions of the International Society for Music Information Retrieval , 2(1), 67–81. DOI: https://doi.org/10.5334/tismir.31

Hill, P. (2002). From score to sound. In Rink, J., editor, Musical Performance – A Guide to Understanding . Cambridge University Press, Cambridge. DOI: https://doi.org/10.1017/CBO9780511811739.010

Howes, P., Callaghan, J., Davis, P., Kenny, D., & Thorpe, W. (2004). The relationship between measured vibrato characteristics and perception in Western operatic singing. Journal of Voice , 18(2), 216–230. DOI: https://doi.org/10.1016/j.jvoice.2003.09.003

Huang, J., & Krumhansl, C. L. (2011). What does seeing the performer add? It depends on musical style, amount of stage behavior, and audience expertise. Musicae Scientiae , 15(3), 343–364. DOI: https://doi.org/10.1177/1029864911414172

Huang, J., & Lerch, A. (2019). Automatic assessment of sight-reading exercises. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Delft, Netherlands.

Huron, D. (2001). Tone and voice: A derivation of the rules of voice-leading from perceptual principles. Music Perception , 19(1), 1–64. DOI: https://doi.org/10.1525/mp.2001.19.1.1

Huron, D. (2015). Affect induction through musical sounds: An ethological perspective. Philosophical Transactions of the Royal Society B: Biological Sciences , 370(1664), 20140098. DOI: https://doi.org/10.1098/rstb.2014.0098

Iyer, V. (2002). Embodied mind, situated cognition, and expressive microtiming in African-American music. Music Perception: An Interdisciplinary Journal , 19(3), 387–414. DOI: https://doi.org/10.1525/mp.2002.19.3.387

Järvinen, T. (1995). Tonal hierarchies in jazz improvisation. Music Perception: An Interdisciplinary Journal , 12(4), 415–437. DOI: https://doi.org/10.2307/40285675

Järvinen, T., & Toiviainen, P. (2000). The effect of metre on the use of tones in jazz improvisation. Musicae Scientiae , 4(1), 55–74. DOI: https://doi.org/10.1177/102986490000400103

Jeong, D., Kwon, T., Kim, Y., Lee, K., & Nam, J. (2019a). VirtuosoNet: A hierarchical RNN-based system for modeling expressive piano performance. In Flexer, A., Peeters, G., Urbano, J., & Volk, A., editors, Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 908–915, Delft, Netherlands.

Jeong, D., Kwon, T., Kim, Y., & Nam, J. (2019b). Graph neural network for music score data and modeling expressive piano performance. In International Conference on Machine Learning, pages 3060–3070.

Juchniewicz, J. (2008). The influence of physical movement on the perception of musical performance. Psychology of Music , 36(4), 417–427. DOI: https://doi.org/10.1177/0305735607086046

Jure, L., Lopez, E., Rocamora, M., Cancela, P., Sponton, H., & Irigaray, I. (2012). Pitch content visualization tools for music performance analysis. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Porto.

Juslin, P. N. (2000). Cue utilization in communication of emotion in music performance: Relating performance to perception. Journal of Experimental Psychology , 26(6), 1797–1813. DOI: https://doi.org/10.1037/0096-1523.26.6.1797

Juslin, P. N. (2003). Five myths about expressivity in music performance and what to do about them. In Proceedings of the International Conference on Arts and Humanities, Honululu, Hawaii.

Juslin, P. N., & Laukka, P. (2003). Communication of emotions in vocal expression and music performance: Different channels, same code? Psychological Bulletin , 129(5), 770–814. DOI: https://doi.org/10.1037/0033-2909.129.5.770

Katz, M. (2004). Capturing Sound: How Technology has Changed Music . University of California Press, Berkeley and Los Angeles.

Kawase, S. (2014). Importance of communication cues in music performance according to performers and audience. International Journal of Psychological Studies , 6(2), 49. DOI: https://doi.org/10.5539/ijps.v6n2p49

Kehling, C., Abeßer, J., Dittmar, C., & Schuller, G. (2014). Automatic tablature transcription of electric guitar recordings by estimation of score- and instrument-related parameters. In Proceedings of the International Conference on Digital Audio Effects (DAFx), Erlangen, Germany.

Kendall, R. A., & Carterette, E. C. (1990). The communication of musical expression. Music Perception , 8(2), 129–164. DOI: https://doi.org/10.2307/40285493

Kirke, A., & Miranda, E. R., editors (2013). Guide to Computing for Expressive Music Performance . Springer Science & Business Media, London. DOI: https://doi.org/10.1007/978-1-4471-4123-5

Knight, T., Upham, F., & Fujinaga, I. (2011). The potential for automatic assessment of trumpet tone quality. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 573–578, Miami, FL.

Kosta, K., Bandtlow, O. F., & Chew, E. (2015). A change-point approach towards representing musical dynamics. In Collins, T., Meredith, D., & Volk, A., editors, Mathematics and Computation in Music, Lecture Notes in Computer Science , pages 179–184, Cham. Springer International Publishing. DOI: https://doi.org/10.1007/978-3-319-20603-5_18

Kosta, K., Bandtlow, O. F., & Chew, E. (2018). Dynamics and relativity: Practical implications of dynamic markings in the score. Journal of New Music Research , 47(5), 438–461. DOI: https://doi.org/10.1080/09298215.2018.1486430

Kosta, K., Ramirez, R., Bandtlow, O., & Chew, E. (2016). Mapping between dynamic markings and performed loudness: A machine learning approach. Journal of Mathematics and Music , 10(2), 149–172. DOI: https://doi.org/10.1080/17459737.2016.1193237

Krumhansl, C. L. (1996). A perceptual analysis of Mozart’s Piano Sonata K. 282: Segmentation, tension, and musical ideas. Music Perception: An Interdisciplinary Journal , 13(3), 401–432. DOI: https://doi.org/10.2307/40286177

Langner, J., & Goebl, W. (2002). Representing expressive performance in tempo-loudness space. In Proceedings of the Conference European Society for the Cognitive Sciences of Music (ESCOM), Liege.

Leman, M., & Maes, P.-J. (2014). The role of embodiment in the perception of music. Empirical Musicology Review , 9(3–4), 236–246. DOI: https://doi.org/10.18061/emr.v9i3-4.4498

Lerch, A. (2009). Software-Based Extraction of Objective Parameters from Music Performances . GRIN Verlag, München.

Lerch, A. (2012). An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics . Wiley-IEEE Press, Hoboken. DOI: https://doi.org/10.1002/9781118393550

Lerch, A., Arthur, C., Pati, A., & Gururani, S. (2019). Music performance analysis: A survey. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Delft.

Li, B., Liu, X., Dinesh, K., Duan, Z., & Sharma, G. (2019). Creating a multitrack classical music performance dataset for multimodal music analysis: Challenges, insights, and applications. IEEE Transactions on Multimedia , 21(2), 522–535. DOI: https://doi.org/10.1109/TMM.2018.2856090

Li, P.-C., Su, L., Yang, Y.-H., & Su, A. W. Y. (2015). Analysis of expressive musical terms in violin using score-informed and expression-based audio features. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 809–815, Malaga, Spain.

Li, S., Dixon, S., & Plumbley, M. D. (2017). Clustering expressive timing with regressed polynomial coefficients demonstrated by a model selection test. In Cunningham, S. J., Duan, Z., Hu, X., & Turnbull, D., editors, Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 457–463, Suzhou, China.

Liem, C. C., & Hanjalic, A. (2011). Expressive timing from cross-performance and audio-based alignment patterns: An extended case study. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Miami, FL.

Liem, C. C., & Hanjalic, A. (2015). Comparative analysis of orchestral performance recordings: An imagebased approach. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Malaga, Spain.

Livingstone, S. R., Thompson, W. F., & Russo, F. A. (2009). Facial expressions and emotional singing: A study of perception and production with motion capture and electromyography. Music Perception , 26(5), 475–488. DOI: https://doi.org/10.1525/mp.2009.26.5.475

Luizard, P., Brauer, E., & Weinzierl, S. (2019). Singing in physical and virtual environments: How performers adapt to room acoustical conditions. In Proceedings of the AES Conference on Immersive and Interactive Audio, York. AES.

Luizard, P., Steffens, J., & Weinzierl, S. (2020). Singing in different rooms: Common or individual adaptation patterns to the acoustic conditions? The Journal of the Acoustical Society of America , 147(2), EL132–EL137. DOI: https://doi.org/10.1121/10.0000715

Luo, Y.-J. (2015). Detection of common mistakes in novice violin playing. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 316–322, Malaga, Spain.

Maempel, H.-J. (2011). Musikaufnahmen als Datenquellen der Interpretationsanalyse. In von Lösch, H., & Weinzierl, S., editors, Gemessene Interpretation – Computergestützte Aufführungsanalyse im Kreuzverhör der Disziplinen, Klang und Begriff , pages 157–171. Schott, Mainz.

Maezawa, A., Yamamoto, K., & Fujishima, T. (2019). Rendering music performance with interpretation variations using conditional variational RNN. In Flexer, A., Peeters, G., Urbano, J., & Volk, A., editors, Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 855–861, Delft, Netherlands.

Malik, I., & Ek, C. H. (2017). Neural translation of musical style. arXiv:1708.03535 [cs] .

Marchini, M., Ramirez, R., Papiotis, P., & Maestre, E. (2014). The sense of ensemble: A machine learning approach to expressive performance modeling in string quartets. Journal of New Music Research , 43(3), 303–317. DOI: https://doi.org/10.1080/09298215.2014.922999

Mayor, O., Bonada, J., & Loscos, A. (2009). Performance analysis and scoring of the singing voice. In Proceedings of the Audio Engineering Society Convention , pages 1–7.

McFee, B., Humphrey, E. J., & Bello, J. P. (2015). A software framework for musical data augmentation. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Malaga, Spain.

McNeil, A. (2017). Seed ideas and creativity in Hindustani Raga music: Beyond the compositionimprovisation dialectic. Ethnomusicology Forum , 26(1), 116–132. DOI: https://doi.org/10.1080/17411912.2017.1304230

McPherson, G. E., & Thompson, W. F. (1998). Assessing music performance: Issues and influences. Research Studies in Music Education , 10(1), 12–24. DOI: https://doi.org/10.1177/1321103X9801000102

Molina, E., Barbancho, I., Gómez, E., Barbancho, A. M., & Tardón, L. J. (2013). Fundamental frequency alignment vs. note-based melodic similarity for singing voice assessment. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 744–748, Vancouver, Canada. DOI: https://doi.org/10.1109/ICASSP.2013.6637747

Müller, M., Konz, V., Bogler, W., & Arifi-Müller, V. (2011). Saarland Music Data (SMD). In Late Breaking Demo (Extended Abstract), International Society for Music Information Retrieval Conference (ISMIR), Miami, FL.

Nakamura, T. (1987). The communication of dynamics between musicians and listeners through musical performance. Perception & Psychophysics , 41(6), 525–533. DOI: https://doi.org/10.3758/BF03210487

Nakano, T., Goto, M., & Hiraga, Y. (2006). An automatic singing skill evaluation method for unknown melodies using pitch interval accuracy and vibrato features. In Proceedings of the International Conference on Spoken Langaunge Processing (INTERSPEECH), volume 12, Pittsburgh, PA.

Narang, K., & Rao, P. (2017). Acoustic features for determining goodness of tabla strokes. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 257–263, Suzhou, China.

Ohriner, M. S. (2012). Grouping hierarchy and trajectories of pacing in performances of Chopin’s Mazurkas. Music Theory Online , 18(1). DOI: https://doi.org/10.30535/mto.18.1.6

Okumura, K., Sako, S., & Kitamura, T. (2011). Stochastic modeling of a musical performance with expressive representations from the musical score. In Klapuri, A., & Leider, C., editors, Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 531–536, Miami, FL. University of Miami.

Oore, S., Simon, I., Dieleman, S., Eck, D., & Simonyan, K. (2020). This time with feeling: Learning expressive musical performance. Neural Computing and Applications , 32(4), 955–967. DOI: https://doi.org/10.1007/s00521-018-3758-9

Ornoy, E., & Cohen, S. (2018). Analysis of contemporary violin recordings of 19th century repertoire: Identifying trends and impacts. Frontiers in Psychology , 9. DOI: https://doi.org/10.3389/fpsyg.2018.02233

Page, K. R., Nurmikko-Fuller, T., Rindfleisch, C., Weigl, D. M., Lewis, R., Dreyfus, L., & De Roure, D. (2015). A toolkit for live annotation of opera performance: Experiences capturing Wagner’s Ring Cycle. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Malaga, Spain.

Palmer, C. (1989). Mapping musical thought to musical performance. Journal of Experimental Psychology: Human Perception and Performance , 15(2), 331–346. DOI: https://doi.org/10.1037/0096-1523.15.2.331

Palmer, C. (1996). On the assignment of structure in music performance. Music Perception: An Interdisciplinary Journal , 14(1), 23–56. DOI: https://doi.org/10.2307/40285708

Palmer, C. (1997). Music performance. Annual Review of Psychology , 48, 115–138. DOI: https://doi.org/10.1146/annurev.psych.48.1.115

Papiotis, P. (2016). A Computational Approach to Studying Interdependence in String Quartet Performance . PhD thesis, Univesitat Pompeu Fabra, Barcelona, Spain.

Pati, K. A., Gururani, S., & Lerch, A. (2018). Assessment of student music performances using deep neural networks. Applied Sciences , 8(4), 507. DOI: https://doi.org/10.3390/app8040507

Peperkamp, J., Hildebrandt, K., & Liem, C. C. (2017). A formalization of relative local tempo variations in collections of performances. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Suzhou.

Pfleiderer, M., Frieler, K., Abeßer, J., Zaddach, W.-G., & Burkhart, B., editors (2017). Inside the Jazzomat: New Perspectives for Jazz Research . Schott Campus.

Pfordresher, P. Q. (2005). Auditory feedback in music performance: The role of melodic structure and musical skill. Journal of Experimental Psychology , 31(6), 1331–1345. DOI: https://doi.org/10.1037/0096-1523.31.6.1331

Pfordresher, P. Q., & Palmer, C. (2002). Effects of delayed auditory feedback on timing of music performance. Psychological Research , 16, 71–79. DOI: https://doi.org/10.1007/s004260100075

Platz, F., & Kopiez, R. (2012). When the eye listens: A meta-analysis of how audio-visual presentation enhances the appreciation of music performance. Music Perception , 30(1), 71–83. DOI: https://doi.org/10.1525/mp.2012.30.1.71

Povel, D.-J. (1977). Temporal structure of performed music: Some preliminary observations. In Acta Psychologica , volume 41, pages 309–320. DOI: https://doi.org/10.1016/0001-6918(77)90024-5

Prögler, J. A. (1995). Searching for swing: Participatory discrepancies in the jazz rhythm section. Ethnomusicology , 39(1), 21–54. DOI: https://doi.org/10.2307/852199

Ramirez, R., Hazan, A., Maestre, E., & Serra, X. (2008). A genetic rule-based model of expressive performance for jazz saxophone. Computer Music Journal , 32(1), 38–50. DOI: https://doi.org/10.1162/comj.2008.32.1.38

Repp, B. H. (1989). Expressive microstructure in music: A preliminary perceptual assessment of four composers’ “pulses”. Music Perception: An Interdisciplinary Journal , 6(3), 243–273. DOI: https://doi.org/10.2307/40285589

Repp, B. H. (1990). Patterns of expressive timing in performances of a Beethoven minuet by nineteen famous pianists. Journal of the Acoustical Society of America (JASA) , 88(2), 622–641. DOI: https://doi.org/10.1121/1.399766

Repp, B. H. (1992). A constraint on the expressive timing of a melodic gesture: Evidence from performance and aesthetic judgment. Music Perception: An Interdisciplinary Journal , 10(2), 221–241. DOI: https://doi.org/10.2307/40285608

Repp, B. H. (1993). Music as motion: A synopsis of Alexander Truslit’s (1938) Gestaltung und Bewegung in der Musik. Psychology of Music , 21(1), 48–72. DOI: https://doi.org/10.1177/030573569302100104

Repp, B. H. (1996a). The art of inaccuracy: Why pianists’ errors are difficult to hear. Music Perception , 14(2), 161–184. DOI: https://doi.org/10.2307/40285716

Repp, B. H. (1996b). The dynamics of expressive piano performance: Schumann’s ‘Träumerei’ revisited. Journal of the Acoustical Society of America (JASA) , 100(1), 641–650. DOI: https://doi.org/10.1121/1.415889

Repp, B. H. (1996c). Pedal timing and tempo in expressive piano performance: A preliminary investigation. Psychology of Music , 24(2), 199–221. DOI: https://doi.org/10.1177/0305735696242011

Repp, B. H. (1997a). The aesthetic quality of a quantitatively average music performance: Two preliminary experiments. Music Perception , 14(4), 419–444. DOI: https://doi.org/10.2307/40285732

Repp, B. H. (1997b). The effect of tempo on pedal timing in piano performance. Psychological Research , 60(3), 164–172. DOI: https://doi.org/10.1007/BF00419764

Repp, B. H. (1998a). A microcosm of musical expression. I. Quantitative analysis of pianists’ timing in the initial measures of Chopin’s Etude in E major. Journal of the Acoustical Society of America (JASA) , 104(2), 1085–1100. DOI: https://doi.org/10.1121/1.423325

Repp, B. H. (1998b). Obligatory “expectations” of expressive timing induced by perception of musical structure. Psychological Research , 61(1), 33–43. DOI: https://doi.org/10.1007/s004260050011

Repp, B. H. (1999). Effects of auditory feedback deprivation on expressive piano performance. Music Perception , 16(4), 409–438. DOI: https://doi.org/10.2307/40285802

Rink, J. (2003). In respect of performance: The view from musicology. Psychology of Music , 31(3), 303–323. DOI: https://doi.org/10.1177/03057356030313004

Roholt, T. C. (2014). Groove: A Phenomenology of Rhythmic Nuance . Bloomsbury Publishing USA. Google-Books-ID: qvJzBAAAQBAJ.

Romani Picas, O., Rodriguez, H. P., Dabiri, D., Tokuda, H., Hariya, W., Oishi, K., & Serra, X. (2015). A real-time system for measuring sound goodness in instrumental sounds. In Proceedings of the Audio Engineering Society Convention, volume 138, Warsaw.

Rosenzweig, S., Scherbaum, F., Shugliashvili, D., Arifi- Müller, V., & Müller, M. (2020). Erkomaishvili Dataset: A curated corpus of traditional Georgian vocal music for computational musicology. Transactions of the International Society for Music Information Retrieval , 3(1), 31–41. DOI: https://doi.org/10.5334/tismir.44

Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology , 39(6), 1161–1178. DOI: https://doi.org/10.1037/h0077714

Sapp, C. S. (2007). Comparative analysis of multiple musical performances. In Proceedings of the International Conference on Music Information Retrieval (ISMIR), Vienna, Austria.

Sapp, C. S. (2008). Hybrid numeric/rank similarity metrics for musical performance analysis. In Proceedings of the International Conference on Music Information Retrieval (ISMIR), Philadelphia, PA.

Sarasúa, A., Caramiaux, B., Tanaka, A., & Ortiz, M. (2017). Datasets for the analysis of expressive musical gestures. In Proceedings of the International Conference on Movement Computing (MOCO), pages 1–4, London, UK. Association for Computing Machinery. DOI: https://doi.org/10.1145/3077981.3078032

Schärer Kalkandjiev, Z., & Weinzierl, S. (2013). The influence of room acoustics on solo music performance: An empirical case study. Acta Acustica united with Acustica , 99(3), 433–441. DOI: https://doi.org/10.3813/AAA.918624

Schärer Kalkandjiev, Z., & Weinzierl, S. (2015). The influence of room acoustics on solo music performance: An experimental study. Psychomusicology: Music, Mind, and Brain , 25(3), 195–207. DOI: https://doi.org/10.1037/pmu0000065

Schramm, R., de Souza Nunes, H., & Jung, C. R. (2015). Automatic Solfège assessment. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 183–189, Malaga, Spain.

Schubert, E., Canazza, S., De Poli, G., & Rodà, A. (2017). Algorithms can mimic human piano performance: The deep blues of music. Journal of New Music Research , 46(2), 175–186. DOI: https://doi.org/10.1080/09298215.2016.1264976

Schubert, E., & Fabian, D. (2006). The dimensions of Baroque music performance: A semantic differential study. Psychology of Music , 34(4), 573–587. DOI: https://doi.org/10.1177/0305735606068105

Schubert, E., & Fabian, D. (2014). A taxonomy of listeners’ judgments of expressiveness in music performance. In Fabian, D., Timmers, R., & Schubert, E., editors, Expressiveness in Music Performance: Empirical Approaches Across Styles and Cultures . Oxford University Press. DOI: https://doi.org/10.1093/acprof:oso/9780199659647.003.0016

Seashore, C. E. (1938). Psychology of Music . McGraw-Hill, New York. DOI: https://doi.org/10.2307/3385515

Serra, X. (2014). Creating research corpora for the computational study of music: The case of the CompMusic Project. In Proceedings of the AES International Conference on Semantic Audio, pages 1–9, London, UK. AES.

Shaffer, L. H. (1984). Timing in solo and duet piano performances. The Quarterly Journal of Experimental Psychology , 36A, 577–595. DOI: https://doi.org/10.1080/14640748408402180

Shi, Z., Sapp, C. S., Arul, K., McBride, J., & Smith, J. O. (2019). SUPRA: Digitizing the Stanford University Piano Roll Archive. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 517–523, Delft, The Netherlands.

Siegwart, H., & Scherer, K. R. (1995). Acoustic concomitants of emotional expression in operatic singing: The case of Lucia in Ardi gli incensi. Journal of Voice , 9(3), 249–260. DOI: https://doi.org/10.1016/S0892-1997(05)80232-2

Silvey, B. A. (2012). The role of conductor facial expression in students’ evaluation of ensemble expressivity. Journal of Research in Music Education , 60(4), 419–429. DOI: https://doi.org/10.1177/0022429412462580

Sloboda, J. A. (1982). Music Performance. In Deutsch, D., editor, The Psychology of Music . Academic Press, New York. DOI: https://doi.org/10.1016/B978-0-12-213562-0.50020-6

Sloboda, J. A. (1983). The communication of musical metre in piano performance. The Quarterly Journal of Experimental Psychology Section A , 35(2), 377–396. DOI: https://doi.org/10.1080/14640748308402140

Srinivasamurthy, A., Holzapfel, A., Ganguli, K. K., & Serra, X. (2017). Aspects of tempo and rhythmic elaboration in Hindustani music: A corpus study. Frontiers in Digital Humanities , 4. DOI: https://doi.org/10.3389/fdigh.2017.00020

Stowell, D., & Chew, E. (2013). Maximum a posteriori estimation of piecewise arcs in tempo time-series. In Aramaki, M., Barthet, M., Kronland-Martinet, R., & Ystad, S., editors, From Sounds to Music and Emotions, Lecture Notes in Computer Science , pages 387–399, Berlin, Heidelberg. Springer. DOI: https://doi.org/10.1007/978-3-642-41248-6_22

Su, L., Yu, L.-F., & Yang, Y.-H. (2014). Sparse cepstral, phase codes for guitar playing technique classification. In Wang, H.-M., Yang, Y.-H., & Lee, J. H., editors, Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 9–14.

Sulem, A., Bodner, E., & Amir, N. (2019). Perceptionbased classification of expressive musical terms: Toward a parameterization of musical expressiveness. Music Perception , 37(2), 147–164. DOI: https://doi.org/10.1525/mp.2019.37.2.147

Sundberg, J. (1993). How can music be expressive? Speech Communication , 13(1), 239–253. DOI: https://doi.org/10.1016/0167-6393(93)90075-V

Sundberg, J. (2018). The singing voice. In Frühholz, S., & Belin, P., editors, The Oxford Handbook of Voice Perception . Oxford University Press. DOI: https://doi.org/10.1093/oxfordhb/9780198743187.013.6

Sundberg, J., Lã, F. M. B., & Himonides, E. (2013). Intonation and expressivity: A single case study of classical Western singing. Journal of Voice , 27(3), 391.e1–391.e8. DOI: https://doi.org/10.1016/j.jvoice.2012.11.009

Takeda, H., Nishimoto, T., & Sagayama, S. (2004). Rhythm and tempo recognition of music performance from a probabilistic approach. In Proceedings of the International Conference on Music Information Retrieval (ISMIR), Barcelona, Spain.

Thompson, S., & Williamon, A. (2003). Evaluating evaluation: Musical performance assessment as a research tool. Music Perception: An Interdisciplinary Journal , 21(1), 21–41. DOI: https://doi.org/10.1525/mp.2003.21.1.21

Timmers, R. (2005). Predicting the similarity between expressive performances of music from measurements of tempo and dynamics. The Journal of the Acoustical Society of America , 117(1), 391–399. DOI: https://doi.org/10.1121/1.1835504

Todd, N. P. M. (1992). The dynamics of dynamics: A model of musical expression. Journal of the Acoustical Society of America , 91, 3540–3550. DOI: https://doi.org/10.1121/1.402843

Todd, N. P. M. (1993). Vestibular feedback in musical performance. Music Perception , 10(3), 379–382. DOI: https://doi.org/10.2307/40285575

Todd, N. P. M. (1995). The kinematics of musical expression. Journal of the Acoustical Society of America (JASA) , 97(3), 1940–1949. DOI: https://doi.org/10.1121/1.412067

Toyoda, K., Noike, K., & Katayose, H. (2004). Utility system for constructing database of performance deviations. In Proceedings of the International Conference on Music Information Retrieval (ISMIR), Barcelona, Spain.

Tsay, C.-J. (2013). Sight over sound in the judgment of music performance. Proceedings of the National Academy of Sciences , 110(36), 14580–14585. DOI: https://doi.org/10.1073/pnas.1221454110

Tzanetakis, G. (2014). Computational ethnomusicology: A music information retrieval perspective. In Proceedings of the Joint International Computer Music/Sound and Music Computing Conference, Athens.

Van Herwaarden, S., Grachten, M., & De Haas, W. B. (2014). Predicting expressive dynamics in piano performances using neural networks. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Taipei, Taiwan.

van Noorden, L., & Moelants, D. (1999). Resonance in the perception of musical pulse. Journal of New Music Research , 28(1), 43–66. DOI: https://doi.org/10.1076/jnmr.28.1.43.3122

Vidwans, A., Gururani, S., Wu, C.-W., Subramanian, V., Swaminathan, R. V., & Lerch, A. (2017). Objective descriptors for the assessment of student music performances. In Proceedings of the AES Conference on Semantic Audio, Erlangen. Audio Engineering Society (AES).

Vieillard, S., Roy, M., & Peretz, I. (2012). Expressiveness in musical emotions. Psychological Research , 76(5), 641–653. DOI: https://doi.org/10.1007/s00426-011-0361-4

Viraraghavan, V. S., Aravind, R., & Murthy, H. A. (2017). A statistical analysis of gamakas in Carnatic music. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Suzhou, China.

Wager, S., Tzanetakis, G., Sullivan, S., Wang, C.-i., Shimmin, J., Kim, M., & Cook, P. (2019). Intonation: A dataset of quality vocal performances refined by spectral clustering on pitch congruence. In Proceedings of the International Conference on Acoustics Speech and Signal Processing (ICASSP), pages 476–480, Brighton, UK. IEEE. DOI: https://doi.org/10.1109/ICASSP.2019.8683554

Wang, B., & Yang, Y.-H. (2019). PerformanceNet: Score-to-audio music generation with multi-band convolutional residual network. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 1174–1181. DOI: https://doi.org/10.1609/aaai.v33i01.33011174

Wang, C., Benetos, E., Lostanlen, V., & Chew, E. (2019). Adaptive time-frequency scattering for periodic modulation recognition in music signals. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Delft, Netherlands.

Wapnick, J., Campbell, L., Siddell-Strebel, J., & Darrow, A.-A. (2009). Effects of non-musical attributes and excerpt duration on ratings of high-level piano performances. Musicae Scientiae , 13(1), 35–54. DOI: https://doi.org/10.1177/1029864909013001002

Wesolowski, B. C. (2016). Timing deviations in jazz performance: The relationships of selected musical variables on horizontal and vertical timing relations: A case study. Psychology of Music , 44(1), 75–94. DOI: https://doi.org/10.1177/0305735614555790

Wesolowski, B. C., Wind, S. A., & Engelhard, G. (2016). Examining rater precision in music performance assessment: An analysis of rating scale structure using the multifaceted Rasch partial credit model. Music Perception: An Interdisciplinary Journal , 33(5), 662–678. DOI: https://doi.org/10.1525/mp.2016.33.5.662

Widmer, G. (2003). Discovering simple rules in complex data: A meta-learning algorithm and some surprising musical discoveries. Artificial Intelligence , 146(2), 129–148. DOI: https://doi.org/10.1016/S0004-3702(03)00016-X

Widmer, G., & Goebl, W. (2004). Computational models of expressive music performance: The state of the art. Journal of New Music Research , 33(3), 203–216. DOI: https://doi.org/10.1080/0929821042000317804

Wilkins, J., Seetharaman, P., Wahl, A., & Pardo, B. (2018). VocalSet: A singing voice dataset. In Gómez, E., Hu, X., Humphrey, E., & Benetos, E., editors, Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 468–474, Paris, France.

Winters, R. M., Gururani, S., & Lerch, A. (2016). Automatic practice logging: Introduction, dataset & preliminary study. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), New York.

Wolf, A., Kopiez, R., Platz, F., Lin, H.-R., & Mütze, H. (2018). Tendency towards the average? The aesthetic evaluation of a quantitatively average music performancea successful replication of Repp’s (1997) study. Music Perception , 36(1), 98–108. DOI: https://doi.org/10.1525/mp.2018.36.1.98

Wu, C.-W., Gururani, S., Laguna, C., Pati, A., Vidwans, A., & Lerch, A. (2016). Towards the objective assessment of music performances. In Proceedings of the International Conference on Music Perception and Cognition (ICMPC), pages 99–103, San Francisco.

Wu, C.-W., & Lerch, A. (2016). On drum playing technique detection in polyphonic mixtures. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), New York. ISMIR.

Wu, C.-W., & Lerch, A. (2018a). Assessment of percussive music performances with feature learning. International Journal of Semantic Computing (IJSC) , 12(3), 315–333. DOI: https://doi.org/10.1142/S1793351X18400147

Wu, C.-W., & Lerch, A. (2018b). From labeled to unlabeled data – on the data challenge in automatic drum transcription. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Paris.

Wu, C.-W., & Lerch, A. (2018c). Learned features for the assessment of percussive music performances. In Proceedings of the International Conference on Semantic Computing (ICSC), Laguna Hills. IEEE. DOI: https://doi.org/10.1109/ICSC.2018.00022

Xia, G., & Dannenberg, R. (2015). Duet interaction: Learning musicianship for automatic accompaniment. In Proceedings of the International Conference on New Interfaces for Musical Expression, NIME 2015, pages 259–264, Baton Rouge, Louisiana, USA. The School of Music and the Center for Computation and Technology (CCT), Louisiana State University.

Xia, G., Wang, Y., Dannenberg, R. B., & Gordon, G. (2015). Spectral learning for expressive interactive ensemble music performance. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 816–822, Malaga, Spain.

Yang, L., Tian, M., & Chew, E. (2015). Vibrato characteristics and frequency histogram envelopes in Beijing opera singing. In Proceedings of the Fifth International Workshop on Folk Music Analysis (FMA), Paris, France.

Zhang, S., Caro Repetto, R., & Serra, X. (2014). Study of the similarity between linguistic tones and melodic pitch contours in Beijing opera singing. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Taipei, Taiwan.

Zhang, S., Caro Repetto, R., & Serra, X. (2015). Predicting pairwise pitch contour relations based on linguistic tone information in Beijing opera singing. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Malaga, Spain.

Zhang, S., Caro Repetto, R., & Serra, X. (2017). Understanding the expressive functions of Jingju metrical patterns through lyrics text mining. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Suzhou, China.

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Electronic Theses and Dissertations

In seeking a definition of mash: attitude in musical style.

Thomas Cassell , East Tennessee State University Follow

Degree Name

MA (Master of Arts)

Appalachian Studies

Date of Award

Committee chair or co-chairs.

Nathaniel Olson

Committee Members

Lee Bidgood, Roy Andrade

“Mash” is a term used to describe one of the most recent major style shifts in the iconic American string band music known as bluegrass. Beginning in the 1990s, the bluegrass sound began to evolve, and ‘mash’ worked its way into the genre as a descriptor of a certain sound. Though a handful of scholars have discussed the social stigmas of the style, no one yet has investigated the simple musical question about mash: what is it?

The purpose of this thesis is to define mash in its musical form through a combination of transcription methods and extensive analysis. Through this research, a recurring set of musical phenomena is identified in the repertoire, related to rhythms, melody, and modality. This study shows the relationship between the downbeat and this music, and identifies and articulates the musical characteristics that define mash as a unique style of bluegrass.

Document Type

Thesis - unrestricted

Recommended Citation

Cassell, Thomas, "In Seeking a Definition of Mash: Attitude in Musical Style" (2021). Electronic Theses and Dissertations. Paper 3916. https://dc.etsu.edu/etd/3916

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