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ISMIR
2004
Springer

Automatic Detection Of Vocal Segments In Popular Songs

14 years 5 months ago
Automatic Detection Of Vocal Segments In Popular Songs
This paper presents a technique for the automatic classification of vocal and non-vocal regions in an acoustic musical signal. The proposed technique uses acoustic features which are suitable to distinguish vocal and non-vocal signals. We employ the Hidden Markov Model (HMM) classifier for vocal and non-vocal classification. In contrast to conventional HMM training methods which employ one model for each class, we create an HMM model space (multi-model HMMs) for segmentation with improved accuracy. In addition, we employ an automatic bootstrapping process which adapts the test song’s own models for better classification accuracy. Experimental evaluations conducted on a database of 20 popular music songs show the validity of the proposed approach.
Tin Lay Nwe, Ye Wang
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where ISMIR
Authors Tin Lay Nwe, Ye Wang
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