Cognitive musicology

Cognitive musicology is a branch of cognitive science concerned with computationally modeling musical knowledge with the goal of understanding both music and cognition.[1]

Cognitive musicology can be differentiated from other branches of music psychology via its methodological emphasis, using computer modeling to study music-related knowledge representation with roots in artificial intelligence and cognitive science. The use of computer models provides an exacting, interactive medium in which to formulate and test theories.[2]

This interdisciplinary field investigates topics such as the parallels between language and music in the brain. Biologically inspired models of computation are often included in research, such as neural networks and evolutionary programs.[3] This field seeks to model how musical knowledge is represented, stored, perceived, performed, and generated. By using a well-structured computer environment, the systematic structures of these cognitive phenomena can be investigated.[4]

Notable researchers

The polymath Christopher Longuet-Higgins, who coined the term "cognitive science", is one of the pioneers of cognitive musicology. Among other things, he is noted for the computational implementation of an early key-finding algorithm.[5] Key finding is an essential element of tonal music, and the key-finding problem has attracted considerable attention in the psychology of music over the past several decades. Carol Krumhansl and Mark Schmuckler proposed an empirically grounded key-finding algorithm which bears their names.[6] Their approach is based on key-profiles which were painstakingly determined by what has come to be known as the probe-tone technique.[7] This algorithm has successfully been able to model the perception of musical key in short excerpts of music, as well as to track listeners' changing sense of key movement throughout an entire piece of music.[8] David Temperley, whose early work within the field of cognitive musicology applied dynamic programming to aspects of music cognition, has suggested a number of refinements to the Krumhansl-Schmuckler Key-Finding Algorithm.[9]

Otto Laske was a champion of cognitive musicology.[10] A collection of papers that he co-edited served to heighten the visibility of cognitive musicology and to strengthen its association with AI and music.[11] The foreword of this book reprints a free-wheeling interview with Marvin Minsky, one of the founding fathers of AI, in which he discusses some of his early writings on music and the mind.[12] AI researcher turned cognitive scientist Douglas Hofstadter has also contributed a number of ideas pertaining to music from an AI perspective.[13] Musician Steve Larson, who worked for a time in Hofstadter's lab, formulated a theory of "musical forces" derived by analogy with physical forces.[14] Hofstadter[15] also weighed in on David Cope's experiments in musical intelligence,[16] which take the form of a computer program called EMI which produces music in the form of, say, Bach, or Chopin, or Cope.

Cope's programs are written in Lisp, which turns out to be a popular language for research in cognitive musicology. Desain and Honing have exploited Lisp in their efforts to tap the potential of microworld methodology in cognitive musicology research.[17] Also working in Lisp, Heinrich Taube has explored computer composition from a wide variety of perspectives.[18] There are, of course, researchers who chose to use languages other than Lisp for their research into the computational modeling of musical processes. Tim Rowe, for example, explores "machine musicianship" through C++ programming.[19] A rather different computational methodology for researching musical phenomena is the toolkit approach advocated by David Huron.[20] At a higher level of abstraction, Gerraint Wiggins has investigated general properties of music knowledge representations such as structural generality and expressive completeness.[21]

Although a great deal of cognitive musicology research features symbolic computation, notable contributions have been made from the biologically inspired computational paradigms. For example, Jamshed Bharucha and Peter Todd have modeled music perception in tonal music with neural networks.[22] Al Biles has applied genetic algorithms to the composition of jazz solos.[23] Numerous researchers have explored algorithmic composition grounded in a wide range of mathematical formalisms.[24][25]

Within cognitive psychology, among the most prominent researchers is Diana Deutsch, who has engaged in a wide variety of work ranging from studies of absolute pitch and musical illusions to the formulation of musical knowledge representations to relationships between music and language.[26] Equally important is Aniruddh D. Patel, whose work combines traditional methodologies of cognitive psychology with neuroscience. Patel is also the author of a comprehensive survey of cognitive science research on music.[27]

Perhaps the most significant contribution to viewing music from a linguistic perspective is the Generative Theory of Tonal Music (GTTM) proposed by Fred Lerdahl and Ray Jackendoff.[28] Although GTTM is presented at the algorithmic level of abstraction rather than the implementational level, their ideas have found computational manifestations in a number of computational projects.[29][30]

For the German-speaking area, Laske's conception of cognitive musicology has been advanced by Uwe Seifert in his book Systematische Musiktheorie und Kognitionswissenschaft. Zur Grundlegung der kognitiven Musikwissenschaft ("Systematic music theory and cognitive science. The foundation of cognitive musicology")[31] and subsequent publications.

See also

References

  1. ↑ Laske, Otto (1999). Navigating New Musical Horizons (Contributions to the Study of Music and Dance). Westport: Greenwood Press. ISBN 978-0-313-30632-7.
  2. ↑ Laske, O. (1999). AI and music: A cornerstone of cognitive musicology. In M. Balaban, K. Ebcioglu, & O. Laske (Eds.), Understanding music with AI: Perspectives on music cognition. Cambridge: The MIT Press.
  3. ↑ Graci, C. (2009-2010) A brief tour of the learning sciences featuring a cognitive tool for investigating melodic phenomena. Journal of Educational Technology Systems, 38(2), 181-211.
  4. ↑ Hamman, M., 1999. "Structure as Performance: Cognitive Musicology and the Objectification of Procedure," in Otto Laske: Navigating New Musical Horizons, ed. J. Tabor. New York: Greenwood Press.
  5. ↑ Longuet-Higgins, C. (1987) Mental Processes: Studies in cognitive science. Cambridge, MA, US: The MIT Press.
  6. ↑ Krumhansl, Carol (1990). Cognitive Foundations of Musical Pitch. Oxford Oxfordshire: Oxford University Press. ISBN 0-19-505475-X.
  7. ↑ Krumhansl, C. and Kessler, E. (1982). Tracing the dynamic changes in perceived tonal organisation in a spatial representation of musical keys. "Psychological Review, 89", 334–368,
  8. ↑ Schmuckler, M. A., & Tomovski, R.(2005) Perceptual tests of musical key-finding. Journal of Experimental Psychology: Human Perception and Performance, 31, 1124–1149,
  9. ↑ Temperley, David (2001). The Cognition of Basic Musical Structures. Cambridge: MIT Press. ISBN 978-0-262-20134-6.
  10. ↑ Laske, Otto (1999). Otto Laske. Westport: Greenwood Press. ISBN 978-0-313-30632-7.
  11. ↑ Balaban, Mira (1992). Understanding Music with AI. Menlo Park: AAAI Press. ISBN 0-262-52170-9.
  12. ↑ Minsky, M. (1981). Music, mind, and meaning. Computer Music Journal, 5(3), 28-44. Retrieved December 1, 2009 from http://web.media.mit.edu/~minsky/papers/MusicMindMeaning.html
  13. ↑ Hofstadter, Douglas (1999). Gödel, Escher, Bach. New York: Basic Books. ISBN 978-0-465-02656-2.
  14. ↑ Larson, S. (2004). Musical Forces and Melodic Expectations: Comparing Computer Models with Experimental Results. "Music Perception, 21" (4), 457–498
  15. ↑ Cope, David (2004). Virtual Music. Cambridge: The MIT Press. ISBN 978-0-262-53261-7.
  16. ↑ Cope, David (1996). Experiments in Musical Intelligence. Madison: A-R Editions. ISBN 978-0-89579-337-9.
  17. ↑ Honing, H. (1993). A microworld approach to formalizing musical knowledge. "Computers and the Humanities, 27" (1), 41–47
  18. ↑ Taube, Heinrich (2004). Notes from the Metalevel. New York: Routledge. ISBN 978-90-265-1975-8.
  19. ↑ Rowe, Robert (2004). Machine Musicianship. City: MIT Pr. ISBN 978-0-262-68149-0.
  20. ↑ Huron, D. (2002). Music Information Processing Using the Humdrum Toolkit: Concepts, Examples, and Lessons. "Computer Music Journal, 26" (2), 11–26.
  21. ↑ Wiggins, G. et al. (1993). A Framework for the Evaluation of Music Representation Systems. "Computer Music Journal, 17" (3), 31–42.
  22. ↑ Bharucha, J. J., & Todd, P. M. (1989). Modeling the perception of tonal structure with neural nets. Computer Music Journal, 44−53
  23. ↑ Biles, J. A. 1994. "GenJam: A Genetic Algorithm for Generating Jazz Solos." Proceedings of the 1994 International Computer Music Conference. San Francisco: International Computer Music Association
  24. ↑ Nierhaus, Gerhard (2008). Algorithmic Composition. Berlin: Springer. ISBN 978-3-211-75539-6.
  25. ↑ Cope, David (2005). Computer Models of Musical Creativity. Cambridge: MIT Press. ISBN 978-0-262-03338-1.
  26. ↑ Deutsch, Diana (1999). The Psychology of Music. Boston: Academic Press. ISBN 978-0-12-213565-1.
  27. ↑ Patel, Aniruddh (1999). Music, Language, and the Brain. Oxford: Oxford University Press. ISBN 978-0-12-213565-1.
  28. ↑ Lerdahl, Fred; Ray Jackendoff (1996). A Generative Theory of Tonal Music. Cambridge: MIT Press. ISBN 978-0-262-62107-6.
  29. ↑ Katz, Jonah; David Pesetsky (May 2009). "The Recursive Syntax and Prosody of Tonal Music" (PDF). Recursion: Structural Complexity in Language and Cognition. Conference at UMass Amherst.
  30. ↑ Hamanaka, Masatoshi; Hirata, Keiji; Tojo, Satoshi (2006). "Implementing 'A Generative Theory of Tonal Music'". Journal of New Music Research 35 (4): 249–277. doi:10.1080/09298210701563238.
  31. ↑ Uwe Seifert: Systematische Musiktheorie und Kognitionswissenschaft. Zur Grundlegung der kognitiven Musikwissenschaft. Orpheus Verlag für systematische Musikwissenschaft, Bonn 1993

Further reading

External links

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