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Computational musicology

January 23, 2023

Computational musicology is an interdisciplinary research area between musicology and computer science.[1] Computational musicology includes any disciplines that use computers in order to study music. It includes sub-disciplines such as mathematical music theory, computer music, systematic musicology, music information retrieval, computational musicology, digital musicology, sound and music computing, and music informatics.[2] As this area of research is defined by the tools that it uses and its subject matter, research in computational musicology intersects with both the humanities and the sciences. The use of computers in order to study and analyze music generally began in the 1960s,[3] although musicians have been using computers to assist them in the composition of music beginning in the 1950s. Today, computational musicology encompasses a wide range of research topics dealing with the multiple ways music can be represented.[4]

History

This history of computational musicology generally began in the middle of the 20th century. Generally, the field is considered to be an extension of a much longer history of intellectual inquiry in music that overlaps with science, mathematics, technology,[5] and archiving.

1960s

Early approaches to computational musicology began in the early 1960s and were being fully developed by 1966.[6][3] At this point in time data entry was done primarily with paper tape or punch cards[3] and was computationally limited. Due to the high cost of this research, in order to be funded projects often tended to ask global questions and look for global solutions.[3] One of the earliest symbolic representation schemes was the Digital Alternate Representations of Music or DARMS. The project was supported by Columbia University and the Ford Foundation between 1964 and 1976.[7] The project was one of the initial large scale projects to develop an encoding scheme that incorporated completeness, objectivity, and encoder-directedness.[7] Other work at this time at Princeton University chiefly driven by Arthur Mendel, and implemented by Michael Kassler and Eric Regener helped push forward the Intermediary Musical Language (IML) and Music Information Retrieval (MIR) languages that later fell out of popularity in the late 1970s. The 1960s also marked a time of documenting bibliographic initiatives such as the Repertoire International de Literature Musicale (RILM) created by Barry Brook in 1967.

1970s

Unlike the global research interests of the 1960s, goals in computational musicology in the 1970s were driven by accomplishing certain tasks.[3] This task driven motivation lead to the development of MUSTRAN for music analysis by lead by Jerome Wenker and Dorothy Gross at Indiana University. Similar projects like SCORE (SCORE-MS) at Stanford University was developed primarily for printing purposes.

1980s

The 1980s were the first decade to move away from centralized computing and move towards that of personalized computing. This transference of resources led to growth in the field as a whole. John Walter Hill began developing a commercial program called Savy PC that was meant to help musicologists analyze lyrical content in music. Findings from Hill's music were able to find patterns in the conversions of sacred and secular texts where only first lines of texts were changed.[3] In keeping with the global questions that dominated the 1960s, Helmuth Schaffrath began his Essen Folk Collection encoded in Essen Associative Code (ESAC) which has since been converted to humdrum notation.[8] Using software developed at the time, Sandra Pinegar examined 13th century music theory manuscripts in her doctoral work at Columbia University in order to gain evidence on the dating and authoring of texts.[9] The 1980s also introduced MIDI notation.

Methods

Computational musicology can be generally divided into the three main branches relating to the three ways music can represented by a computer: sheet music data, symbolic data, and audio data. Sheet music data refers to the human-readable, graphical representation of music via symbols. Examples of this branch of research would include digitizing scores ranging from 15th Century neumenal notation to contemporary Western music notation. Like sheet music data, symbolic data refers to musical notation in a digital format, but symbolic data is not human readable and is encoded in order to be parsed by a computer. Examples of this type of encoding include piano roll, kern,[10] and MIDI representations. Lastly, audio data refers to recording of the representations of the acoustic wave or sound that results from changes in the oscillations of air pressure.[11] Examples of this type of encoding include MP3 or WAV files.

Sheet Music Data

Sheet music is meant to be read by the musician or performer. Generally, the term refers to the standardized nomenclature used by a culture to document their musical notation. In addition to music literacy, musical notation also demands choices from the performer. For example, the notation of Hindustani ragas will begin with an alap that does not demand a strict adherence to a beat or pulse, but is left up to the discretion of the performer.[12] The sheet music notation captures the sequence of gestures the performer is encouraged to make within a musical culture, but is by no means fixed to those performance choices.

Symbolic Data

Symbolic data refers to musical encoding that is able to be parsed by a computer. Unlike sheet music data, Any type of digital data format may be regarded as symbolic due to the fact that the system that is representing it is generated from a finite series of symbols. Symbolic data typically does not have any sort of performative choices required on the part of the performer.[4] Two of the most common software choices for analyzing symbolic data are David Huron's Humdrum Toolkit[13] and Michael Scott Cuthbert and Christopher Azaria's music21.[14]

Audio Data

Audio data is generally conceptualized as existing on a continuum of features ranging from lower to higher level audio features. Low-level audio features refer to loudness, spectral flux, and cepstrum. Mid-level audio features refer to pitch, onsets, and beats. Examples of high-level audio features include style, artist, mood, and key.[15]

Applications

Music databases

One of the earliest applications in computational musicology was the creation and use of musical databases. Input, usage and analysis of large amounts of data can be very troublesome using manual methods while usage of computers can make such tasks considerably easier.

Analysis of music

Different computer programs have been developed to analyze musical data. Data formats vary from standard notation to raw audio. Analysis of formats that are based on storing all properties of each note, for example MIDI, were used originally and are still among the most common methods. Significant advances in analysis of raw audio data have been made only recently.

Artificial production of music

Different algorithms can be used to both create complete compositions and improvise music. One of the methods by which a program can learn improvisation is analysis of choices a human player makes while improvising. Artificial neural networks are used extensively in such applications.

Historical change and music

One developing sociomusicological theory in computational musicology is the "Discursive Hypothesis" proposed by Kristoffer Jensen and David G. Hebert, which suggests that "because both music and language are cultural discourses (which may reflect social reality in similarly limited ways), a relationship may be identifiable between the trajectories of significant features of musical sound and linguistic discourse regarding social data."[16] According to this perspective, analyses of "big data" may improve our understandings of how particular features of music and society are interrelated and change similarly across time, as significant correlations are increasingly identified within the musico-linguistic spectrum of human auditory communication.[17]

Non-western music

Strategies from computational musicology are recently being applied for analysis of music in various parts of the world. For example, professors affiliated with the Birla Institute of Technology in India have produced studies of harmonic and melodic tendencies (in the raga structure) of Hindustani classical music.[18]

Research

RISM's (Répertoire International des Sources Musicales) database is one of the world's largest music databases, containing over 700,000 references to musical manuscripts. Anyone can use its search engine to find compositions.[19]

The Centre for History and Analysis of Recorded Music (CHARM) has developed the Mazurka Project,[20] which offers "downloadable recordings . . . analytical software and training materials, and a variety of resources relating to the history of recording."

Computational musicology in popular culture

Research from computational musicology occasionally is the focus of popular culture and major news outlets. Examples of this include reporting in The New Yorker musicologists Nicholas Cook and Craig Sapp while working on the Centre for the History and Analysis of Recorded Music (CHARM), at the University of London discovered the fraudulent recording of pianist Joyce Hatto.[21] On the 334th birthday of Johann Sebastian Bach, Google celebrated the occasion with a Google Doodle that allowed individuals to enter their own score into the interface, then have a machine learning model called Coconet[22] harmonize the melody.[23]

See also

References

  1. ^ "Unfolding the Potential of Computational Musicology" (PDF). Proceedings of the Thirteenth International Conference on Informatics and Semiotics in Organisations: Problems and Possibilities of Computational Humanities.
  2. ^ Meredith, David (2016). "Preface". Computational Music Analysis. New York: Springer. p. v. ISBN 978-3319259291.
  3. ^ a b c d e f Hewlett, Walter B.; Selfridge-Field, Eleanor (1991). "Computing in Musicology, 1966-91". Computers and the Humanities. 25 (6): 381–392. doi:10.1007/BF00141188. JSTOR 30208121. S2CID 30204949.
  4. ^ a b Meinard, Müller (2015-07-21). Fundamentals of music processing : audio, analysis, algorithms, applications. Switzerland. ISBN 9783319219455. OCLC 918555094.
  5. ^ Forte, Allen (1967). "Music and computing: the present situation". Computers and the Humanities. 2 (1): 32–35. doi:10.1007/BF02402463. JSTOR 30203948. S2CID 33681168.
  6. ^ Berlind, Gary; Brook, Barry S.; Hiller, Lejaren A.; Larue, Jan P.; Logemann, George W. (Fall 1966). "Writings on the Use of Computers in Music". College Music Symposium. 6: 143–157. JSTOR 40373186.
  7. ^ a b Erickson, Raymond F. (1975). ""The Darms Project": A Status Report". Computers and the Humanities. 9 (6): 291–298. doi:10.1007/BF02396292. JSTOR 30204239. S2CID 62220033.
  8. ^ "ESAC Data Homepage". www.esac-data.org. Retrieved 2019-02-11.
  9. ^ "Textual and conceptual relationships among theoretical writings on measurable music of the thirteenth and early fourteenth centuries - ProQuest". ProQuest 303944932. {{cite web}}: Missing or empty |url= (help)
  10. ^ Huron, David (2002). "Music information processing using the Humdrum Toolkit: Concepts, examples, and lessons". Computer Music Journal. 26 (2): 11–26. doi:10.1162/014892602760137158. S2CID 25996361.
  11. ^ Müller, Meinard (2015), "Music Representations", in Müller, Meinard (ed.), Fundamentals of Music Processing: Audio, Analysis, Algorithms, Applications, Springer International Publishing, pp. 1–37, doi:10.1007/978-3-319-21945-5_1, ISBN 9783319219455
  12. ^ The raga guide : a survey of 74 Hindustani ragas, Bor, Joep., Rao, Suvarnalata, 1954-, Meer, Wim van der., Harvey, Jane, 1949-, Chaurasia, Hariprasad., Das Gupta, Buddhadev, 1933-, Nimbus Records, 2002, ISBN 978-0954397609, OCLC 80291538{{citation}}: CS1 maint: others (link)
  13. ^ "The Humdrum Toolkit: Software for Music Research — humdrum-tools 1 documentation". www.humdrum.org. Retrieved 2019-03-20.
  14. ^ Cuthbert, Michael Scott; Ariza, Christopher (August 2010). "music21: A Toolkit for Computer-Aided Musicology and Symbolic Music Data". In J. Stephen Downie; Remco C. Veltkamp (eds.). 11th International Society for Music Information Retrieval Conference (ISMIR 2010), August 9-13, 2010, Utrecht, Netherlands. pp. 637–642. hdl:1721.1/84963. ISBN 9789039353813.
  15. ^ Pablo Bello, Juan. "Low-level features and timbre" (PDF). nyu.edu. Retrieved 2019-02-11.
  16. ^ McCollum, Jonathan and Hebert, David (2014) Theory and Method in Historical Ethnomusicology Lanham, MD: Lexington Books / Rowman & Littlefield ISBN 0739168266; p.62. Some of Jensen and Hebert’s pioneering findings from 2013 on tendencies in US Billboard Hot 100 songs have since been replicated and expanded upon by other scholars (e.g. Mauch M, MacCallum RM,Levy M, Leroi AM. 2015 The evolution of popular music: USA 1960–2010. R. Soc. Open sci. 2: 150081. https://dx.doi.org/10.1098/rsos.150081).
  17. ^ Kristoffer Jensen and David G. Hebert (2016). Evaluation and Prediction of Harmonic Complexity Across 76 Years of Billboard 100 Hits. In R. Kronland-Martinet, M. Aramaki, and S. Ystad, (Eds.), Music, Mind, and Embodiment. Switzerland: Springer Press, pp.283-296. ISBN 978-3-319-46281-3.
  18. ^ Chakraborty, S., Mazzola, G., Tewari, S., Patra, M. (2014) "Computational Musicology in Hindustani Music" New York: Springer.
  19. ^ RISM database, <http://www.rism.info/>
  20. ^ Mazurka Project, <http://mazurka.org.uk/>
  21. ^ Singer, Mark (2007-09-10). "Fantasia for Piano". The New Yorker. ISSN 0028-792X. Retrieved 2019-03-23.
  22. ^ Huang, Cheng-Zhi Anna; Cooijmans, Tim; Roberts, Adam; Courville, Aaron; Eck, Douglas (2019-03-17). "Counterpoint by Convolution". arXiv:1903.07227 [cs.LG].
  23. ^ "Coconet: The ML model behind today's Bach Doodle". magenta.tensorflow.org. Retrieved 2019-03-23.

External links

  • Computational Musicology: A Survey on Methodologies and Applications
  • Towards the compleat musicologist?
  • Transforming Musicology: An AHRC Digital Transformations project

January 23, 2023
computational, musicology, interdisciplinary, research, area, between, musicology, computer, science, includes, disciplines, that, computers, order, study, music, includes, disciplines, such, mathematical, music, theory, computer, music, systematic, musicology. Computational musicology is an interdisciplinary research area between musicology and computer science 1 Computational musicology includes any disciplines that use computers in order to study music It includes sub disciplines such as mathematical music theory computer music systematic musicology music information retrieval computational musicology digital musicology sound and music computing and music informatics 2 As this area of research is defined by the tools that it uses and its subject matter research in computational musicology intersects with both the humanities and the sciences The use of computers in order to study and analyze music generally began in the 1960s 3 although musicians have been using computers to assist them in the composition of music beginning in the 1950s Today computational musicology encompasses a wide range of research topics dealing with the multiple ways music can be represented 4 Contents 1 History 1 1 1960s 1 2 1970s 1 3 1980s 2 Methods 2 1 Sheet Music Data 2 2 Symbolic Data 2 3 Audio Data 3 Applications 3 1 Music databases 3 2 Analysis of music 3 3 Artificial production of music 3 4 Historical change and music 3 5 Non western music 4 Research 5 Computational musicology in popular culture 6 See also 7 References 8 External linksHistory EditThis history of computational musicology generally began in the middle of the 20th century Generally the field is considered to be an extension of a much longer history of intellectual inquiry in music that overlaps with science mathematics technology 5 and archiving 1960s Edit Early approaches to computational musicology began in the early 1960s and were being fully developed by 1966 6 3 At this point in time data entry was done primarily with paper tape or punch cards 3 and was computationally limited Due to the high cost of this research in order to be funded projects often tended to ask global questions and look for global solutions 3 One of the earliest symbolic representation schemes was the Digital Alternate Representations of Music or DARMS The project was supported by Columbia University and the Ford Foundation between 1964 and 1976 7 The project was one of the initial large scale projects to develop an encoding scheme that incorporated completeness objectivity and encoder directedness 7 Other work at this time at Princeton University chiefly driven by Arthur Mendel and implemented by Michael Kassler and Eric Regener helped push forward the Intermediary Musical Language IML and Music Information Retrieval MIR languages that later fell out of popularity in the late 1970s The 1960s also marked a time of documenting bibliographic initiatives such as the Repertoire International de Literature Musicale RILM created by Barry Brook in 1967 1970s Edit Unlike the global research interests of the 1960s goals in computational musicology in the 1970s were driven by accomplishing certain tasks 3 This task driven motivation lead to the development of MUSTRAN for music analysis by lead by Jerome Wenker and Dorothy Gross at Indiana University Similar projects like SCORE SCORE MS at Stanford University was developed primarily for printing purposes 1980s Edit The 1980s were the first decade to move away from centralized computing and move towards that of personalized computing This transference of resources led to growth in the field as a whole John Walter Hill began developing a commercial program called Savy PC that was meant to help musicologists analyze lyrical content in music Findings from Hill s music were able to find patterns in the conversions of sacred and secular texts where only first lines of texts were changed 3 In keeping with the global questions that dominated the 1960s Helmuth Schaffrath began his Essen Folk Collection encoded in Essen Associative Code ESAC which has since been converted to humdrum notation 8 Using software developed at the time Sandra Pinegar examined 13th century music theory manuscripts in her doctoral work at Columbia University in order to gain evidence on the dating and authoring of texts 9 The 1980s also introduced MIDI notation Methods EditComputational musicology can be generally divided into the three main branches relating to the three ways music can represented by a computer sheet music data symbolic data and audio data Sheet music data refers to the human readable graphical representation of music via symbols Examples of this branch of research would include digitizing scores ranging from 15th Century neumenal notation to contemporary Western music notation Like sheet music data symbolic data refers to musical notation in a digital format but symbolic data is not human readable and is encoded in order to be parsed by a computer Examples of this type of encoding include piano roll kern 10 and MIDI representations Lastly audio data refers to recording of the representations of the acoustic wave or sound that results from changes in the oscillations of air pressure 11 Examples of this type of encoding include MP3 or WAV files Sheet Music Data Edit Sheet music is meant to be read by the musician or performer Generally the term refers to the standardized nomenclature used by a culture to document their musical notation In addition to music literacy musical notation also demands choices from the performer For example the notation of Hindustani ragas will begin with an alap that does not demand a strict adherence to a beat or pulse but is left up to the discretion of the performer 12 The sheet music notation captures the sequence of gestures the performer is encouraged to make within a musical culture but is by no means fixed to those performance choices Symbolic Data Edit Symbolic data refers to musical encoding that is able to be parsed by a computer Unlike sheet music data Any type of digital data format may be regarded as symbolic due to the fact that the system that is representing it is generated from a finite series of symbols Symbolic data typically does not have any sort of performative choices required on the part of the performer 4 Two of the most common software choices for analyzing symbolic data are David Huron s Humdrum Toolkit 13 and Michael Scott Cuthbert and Christopher Azaria s music21 14 Audio Data Edit Audio data is generally conceptualized as existing on a continuum of features ranging from lower to higher level audio features Low level audio features refer to loudness spectral flux and cepstrum Mid level audio features refer to pitch onsets and beats Examples of high level audio features include style artist mood and key 15 Applications EditMusic databases Edit One of the earliest applications in computational musicology was the creation and use of musical databases Input usage and analysis of large amounts of data can be very troublesome using manual methods while usage of computers can make such tasks considerably easier Analysis of music Edit Different computer programs have been developed to analyze musical data Data formats vary from standard notation to raw audio Analysis of formats that are based on storing all properties of each note for example MIDI were used originally and are still among the most common methods Significant advances in analysis of raw audio data have been made only recently Artificial production of music Edit Different algorithms can be used to both create complete compositions and improvise music One of the methods by which a program can learn improvisation is analysis of choices a human player makes while improvising Artificial neural networks are used extensively in such applications Historical change and music Edit One developing sociomusicological theory in computational musicology is the Discursive Hypothesis proposed by Kristoffer Jensen and David G Hebert which suggests that because both music and language are cultural discourses which may reflect social reality in similarly limited ways a relationship may be identifiable between the trajectories of significant features of musical sound and linguistic discourse regarding social data 16 According to this perspective analyses of big data may improve our understandings of how particular features of music and society are interrelated and change similarly across time as significant correlations are increasingly identified within the musico linguistic spectrum of human auditory communication 17 Non western music Edit Strategies from computational musicology are recently being applied for analysis of music in various parts of the world For example professors affiliated with the Birla Institute of Technology in India have produced studies of harmonic and melodic tendencies in the raga structure of Hindustani classical music 18 Research EditRISM s Repertoire International des Sources Musicales database is one of the world s largest music databases containing over 700 000 references to musical manuscripts Anyone can use its search engine to find compositions 19 The Centre for History and Analysis of Recorded Music CHARM has developed the Mazurka Project 20 which offers downloadable recordings analytical software and training materials and a variety of resources relating to the history of recording Computational musicology in popular culture EditResearch from computational musicology occasionally is the focus of popular culture and major news outlets Examples of this include reporting in The New Yorker musicologists Nicholas Cook and Craig Sapp while working on the Centre for the History and Analysis of Recorded Music CHARM at the University of London discovered the fraudulent recording of pianist Joyce Hatto 21 On the 334th birthday of Johann Sebastian Bach Google celebrated the occasion with a Google Doodle that allowed individuals to enter their own score into the interface then have a machine learning model called Coconet 22 harmonize the melody 23 See also EditAlgorithmic composition Computer models of musical creativity Music cognition Cognitive musicology Musicology Artificial neural network MIDI JFugueReferences Edit Unfolding the Potential of Computational Musicology PDF Proceedings of the Thirteenth International Conference on Informatics and Semiotics in Organisations Problems and Possibilities of Computational Humanities Meredith David 2016 Preface Computational Music Analysis New York Springer p v ISBN 978 3319259291 a b c d e f Hewlett Walter B Selfridge Field Eleanor 1991 Computing in Musicology 1966 91 Computers and the Humanities 25 6 381 392 doi 10 1007 BF00141188 JSTOR 30208121 S2CID 30204949 a b Meinard Muller 2015 07 21 Fundamentals of music processing audio analysis algorithms applications Switzerland ISBN 9783319219455 OCLC 918555094 Forte Allen 1967 Music and computing the present situation Computers and the Humanities 2 1 32 35 doi 10 1007 BF02402463 JSTOR 30203948 S2CID 33681168 Berlind Gary Brook Barry S Hiller Lejaren A Larue Jan P Logemann George W Fall 1966 Writings on the Use of Computers in Music College Music Symposium 6 143 157 JSTOR 40373186 a b Erickson Raymond F 1975 The Darms Project A Status Report Computers and the Humanities 9 6 291 298 doi 10 1007 BF02396292 JSTOR 30204239 S2CID 62220033 ESAC Data Homepage www esac data org Retrieved 2019 02 11 Textual and conceptual relationships among theoretical writings on measurable music of the thirteenth and early fourteenth centuries ProQuest ProQuest 303944932 a href Template Cite web html title Template Cite web cite web a Missing or empty url help Huron David 2002 Music information processing using the Humdrum Toolkit Concepts examples and lessons Computer Music Journal 26 2 11 26 doi 10 1162 014892602760137158 S2CID 25996361 Muller Meinard 2015 Music Representations in Muller Meinard ed Fundamentals of Music Processing Audio Analysis Algorithms Applications Springer International Publishing pp 1 37 doi 10 1007 978 3 319 21945 5 1 ISBN 9783319219455 The raga guide a survey of 74 Hindustani ragas Bor Joep Rao Suvarnalata 1954 Meer Wim van der Harvey Jane 1949 Chaurasia Hariprasad Das Gupta Buddhadev 1933 Nimbus Records 2002 ISBN 978 0954397609 OCLC 80291538 a href Template Citation html title Template Citation citation a CS1 maint others link The Humdrum Toolkit Software for Music Research humdrum tools 1 documentation www humdrum org Retrieved 2019 03 20 Cuthbert Michael Scott Ariza Christopher August 2010 music21 A Toolkit for Computer Aided Musicology and Symbolic Music Data In J Stephen Downie Remco C Veltkamp eds 11th International Society for Music Information Retrieval Conference ISMIR 2010 August 9 13 2010 Utrecht Netherlands pp 637 642 hdl 1721 1 84963 ISBN 9789039353813 Pablo Bello Juan Low level features and timbre PDF nyu edu Retrieved 2019 02 11 McCollum Jonathan and Hebert David 2014 Theory and Method in Historical Ethnomusicology Lanham MD Lexington Books Rowman amp Littlefield ISBN 0739168266 p 62 Some of Jensen and Hebert s pioneering findings from 2013 on tendencies in US Billboard Hot 100 songs have since been replicated and expanded upon by other scholars e g Mauch M MacCallum RM Levy M Leroi AM 2015 The evolution of popular music USA 1960 2010 R Soc Open sci 2 150081 https dx doi org 10 1098 rsos 150081 Kristoffer Jensen and David G Hebert 2016 Evaluation and Prediction of Harmonic Complexity Across 76 Years of Billboard 100 Hits In R Kronland Martinet M Aramaki and S Ystad Eds Music Mind and Embodiment Switzerland Springer Press pp 283 296 ISBN 978 3 319 46281 3 Chakraborty S Mazzola G Tewari S Patra M 2014 Computational Musicology in Hindustani Music New York Springer RISM database lt http www rism info gt Mazurka Project lt http mazurka org uk gt Singer Mark 2007 09 10 Fantasia for Piano The New Yorker ISSN 0028 792X Retrieved 2019 03 23 Huang Cheng Zhi Anna Cooijmans Tim Roberts Adam Courville Aaron Eck Douglas 2019 03 17 Counterpoint by Convolution arXiv 1903 07227 cs LG Coconet The ML model behind today s Bach Doodle magenta tensorflow org Retrieved 2019 03 23 External links EditComputational Musicology A Survey on Methodologies and Applications Towards the compleat musicologist Transforming Musicology An AHRC Digital Transformations project Retrieved from https en wikipedia org w index php title Computational musicology amp oldid 1068630813, wikipedia, wiki, book, books, library,

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