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.

Authors: Sandler, Mark; Aucouturier, Jean-Julien
Affiliation: Department of Electronic Engineering, King’s College, London, UK
AES Convention: 110 (May 2001) Paper Number: 5379
Publication Date: May 1, 2001
Subject: Signal Processing for Audio

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