In this research, it is proposed that music can be thought of as random processes, and music style recognition can be thought of as a system identification problem. Then, a general framework for modeling music using Markov chains is described, and based on this framework, a two-way composer identification scheme is demonstrated. The scheme utilizes the Kullback-Leibler distance as the metric between distribution functions, and it is shown that under the condition when the marginals are identical, the scheme gives maximum likelihood identification. Experiments of composer identification are conducted on all the string quartets written by Mozart and Haydn, and the results are documented and discussed.
J. Kepler. Oldenbourg, München, 5., unveränd. reprograf. Nachdr. d. Ausg. von 1939 Edition, (1990)Übers. u. eingeleitet von Max Caspar. Hrsg. im Auftr. d. Bayer. Akad. d. Wiss. in München.
J. Kilian, und H. Hoos. Proc. International Computer Music Conference 2000 (ICMC-2000), Seite 454--457. International Computer Music Association (ICMA), San Francisco, CA, (2000)
H. Hoos, K. Renz, und M. Görg. Proc. 2nd International Symposium on Music Information Retrieval (ISMIR 2001), Seite 41--50. Indiana University, Bloomington (IN), USA, (2001)