Computational musicology: Difference between revisions
→Applications: added non-western example |
|||
| Line 19: | Line 19: | ||
=== Historical change and music === |
=== Historical change and music === |
||
Analyses of "[[big data]]" may potentially improve our understandings of how particular features of music and society are interrelated and change similarly across time. One pioneering sociomusicological theory proposed by Kristoffer Jensen and [[David G. Hebert]] 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 across time."<ref>McCollum, Jonathan and Hebert, David (2014) [http://www.amazon.com/Theory-Historical-Ethnomusicology-Jonathan-McCollum/dp/0739168266 "Theory and Method in Historical Ethnomusicology"] Lanham, MD: Lexington Books / Rowman & Littlefield ISBN 0739168266; p.62</ref> |
Analyses of "[[big data]]" may potentially improve our understandings of how particular features of music and society are interrelated and change similarly across time. One pioneering sociomusicological theory proposed by Kristoffer Jensen and [[David G. Hebert]] 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 across time."<ref>McCollum, Jonathan and Hebert, David (2014) [http://www.amazon.com/Theory-Historical-Ethnomusicology-Jonathan-McCollum/dp/0739168266 "Theory and Method in Historical Ethnomusicology"] Lanham, MD: Lexington Books / Rowman & Littlefield ISBN 0739168266; p.62</ref> |
||
=== 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 music.<ref>Chakraborty, S., Mazzola, G., Tewari, S., Patra, M. (2014) [http://www.springer.com/gp/book/9783319114712 "Computational Musicology in Hindustani Music"] New York: Springer.</ref> |
|||
== Research == |
== Research == |
||
Revision as of 08:57, 21 February 2015
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 utilize 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.
History
Lejaren Hiller acted as one of the foremost pioneers by creating one of the first musical compositions with a computer in 1957.[2] 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.[3]
Methods
Most of the work in computational musicology is done with computers that run specifically designed programs. Commonly they utilize theory and methods from 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 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
Analyses of "big data" may potentially improve our understandings of how particular features of music and society are interrelated and change similarly across time. One pioneering sociomusicological theory proposed by Kristoffer Jensen and David G. Hebert 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 across time."[4]
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 music.[5]
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.[6]
See also
References
- ^ Coutinho, Gimenes, Martins and Miranda (2005): "Computational Musicology: An Artificial Life Approach", <http://cmr.soc.plymouth.ac.uk/publications/computationalmusicology.pdf>
- ^ Lejaren Hiller Wikipedia article, Lejaren Hiller
- ^ Laine, Pauli (2005): "Tietokoneavusteisen musiikintutkimuksen menetelmistä" (in Finnish)<http://www.musiikkilehti.fi/1-2005/2.pdf>
- ^ McCollum, Jonathan and Hebert, David (2014) "Theory and Method in Historical Ethnomusicology" Lanham, MD: Lexington Books / Rowman & Littlefield ISBN 0739168266; p.62
- ^ Chakraborty, S., Mazzola, G., Tewari, S., Patra, M. (2014) "Computational Musicology in Hindustani Music" New York: Springer.
- ^ RISM database, <http://www.rism.info/>
- ^ Mazurka Project, <http://mazurka.org.uk/>