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 Import into BibTeX
Subject: Signal Processing for Audio
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=9928

Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!

This paper costs $33 for non-members and is free for AES members and E-Library subscribers.

Learn more about the AES E-Library

E-Library Location: (CD aes18)   /pp2001/pp0105/110100.pdf

Start a discussion about this paper!