Computational musicology

Computational musicology is defined as the study of music with computational modelling and simulation.[1] It saw its beginning in the 1950s and originally did not use computers, but more of statistical and mathematical methods. Nowadays computational musicology depends mostly on complex algorithms to either go through vast amounts of information or produce music using given parameters. Several alternative names and subdisciplines of the field include mathematical music theory, computer music, systematic musicology, music information retrieval, computational musicology, digital musicology, sound and music computing and music informatics.[2]

History

Lejaren Hiller acted as one of the foremost pioneers by creating one of the first musical compositions with a computer in 1957.[3] In the 1960s research continued using statistical and mathematical methods, and started to use computers in an increasing manner as their capabilities grew. 1970s and 1980s were especially significant times for computational musicology as many discoveries were made. Since then the field has suffered a general lack of interest.[4]

Methods

Most of the work in computational musicology is done with computers that run specifically designed programs. Commonly they employ the theory and methods statistical science, mathematics and music theory. Comprehension of the physics of hearing and sound are also required in analysis of raw audio data.

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."[5] 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.[6]

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.[7]

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.[8]

The Centre for History and Analysis of Recorded Music (CHARM) has developed the Mazurka Project,[9] which offers "downloadable recordings . . . analytical software and training materials, and a variety of resources relating to the history of recording."

See also

References

  1. ↑ Coutinho, Gimenes, Martins and Miranda (2005): "Computational Musicology: An Artificial Life Approach", <http://cmr.soc.plymouth.ac.uk/publications/computationalmusicology.pdf>
  2. ↑ Meredith, David (2016). "Preface". Computational Music Analysis. New York: Springer. p. v. ISBN 978-3319259291.
  3. ↑ Lejaren Hiller Wikipedia article, Lejaren Hiller
  4. ↑ Laine, Pauli (2005): "Tietokoneavusteisen musiikintutkimuksen menetelmistä" (in Finnish)<http://www.musiikkilehti.fi/1-2005/2.pdf>
  5. ↑ 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. http://dx.doi.org/10.1098/rsos.150081).
  6. ↑ 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.
  7. ↑ Chakraborty, S., Mazzola, G., Tewari, S., Patra, M. (2014) "Computational Musicology in Hindustani Music" New York: Springer.
  8. ↑ RISM database, <http://www.rism.info/>
  9. ↑ Mazurka Project, <http://mazurka.org.uk/>
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.