Modeling Music as Markov Chains: Composer Identiflcation
Eleanor Selfridge-Field, Craig Stuart Sapp · 2006
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 identiflcation problem. Then, a general framework for modeling music using Markov chains is described, and based on this framework, a two-way composer identiflcation 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 identiflcation. Experiments of composer identiflcation are conducted on all the string quartets written by Mozart and Haydn, and the results are documented and discussed.