A music structure inference algorithm based on symbolic data analysis

Gabriel Sargent 1 Stanislaw Raczynski 2 Frédéric Bimbot 1 Emmanuel Vincent 1 Shigeki Sagayama 2
1 METISS - Speech and sound data modeling and processing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : The present document describes a music structure inference algorithm submitted to the MIREX 2011 evaluation campaign (structural segmentation task). It consists of 3 stages : symbolic feature extraction, structural segment boundary estimation, and structural segment clustering. We consider as inputs chord estimations from the system of Ueda et al., expressed at the 2-beat scale. Beats and downbeats are estimated by the system of Davies et al. The structural segmentation step uses a regularity-constrained Viterbi approach. It assumes that the structure of pop songs is generally based on a few typical segments, whose sizes are called structural pulsation periods. The segments are then clustered according to their similarity, through the minimization of an adaptive model selection criterion.
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Poster
MIREX - ISMIR 2011, Oct 2011, Miami, United States
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https://hal.archives-ouvertes.fr/hal-00618141
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Dernière modification le : jeudi 21 mars 2019 - 14:20:12
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Gabriel Sargent, Stanislaw Raczynski, Frédéric Bimbot, Emmanuel Vincent, Shigeki Sagayama. A music structure inference algorithm based on symbolic data analysis. MIREX - ISMIR 2011, Oct 2011, Miami, United States. 〈hal-00618141〉

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