Current models for capturing metric structure of recordings of music are concerned primarily with the task of tempo and beat estimation. Even though these models have the potential for extracting other metric and rhythmic information, this potential has not been realized. In this paper, a model for describing the general metric structure of audio signals and behavioral data is presented. This model employs reson filters, rather than the comb filters used in earlier models. The oscillatory nature...
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.