Aucouturier, J.-J. and Sandler, M. Segmentation of Musical Signals Using Hidden Markov Models. Proceedings of the Audio Engineering Society 110th Convention, May 2001

Sony CSL authors: Jean-Julien Aucouturier

Abstract

In this paper, we present a segmentation algorithm for acoustic musical signals, using a hidden Markov model. Through unsupervised learning, we discover regions in the music that present steady statistical properties: textures. We investigate different front-ends for the system, and compare their performances. We then show that the obtained segmentation often translates a structure explained by musicology: chorus and verse, different instrumental sections, etc. Finally, we discuss the necessity of the HMM and conclude that an efficient segmentation of music is more than a static clustering and should make use of the dynamics of the data.

Keywords: music, segmentation, timbre

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BibTeX entry

@INPROCEEDINGS { aucouturier:01a, AUTHOR="Aucouturier, J.-J. and Sandler, M.", BOOKTITLE="Proceedings of the Audio Engineering Society 110th Convention", MONTH="May", TITLE="Segmentation of Musical Signals Using Hidden Markov Models", YEAR="2001", }