@inproceedings{e199f4e117a7415fbff76412445ff19b,
title = "Vocal detection in music with support vector machines",
abstract = "We propose a statistical learning approach for the automatic detection of vocal regions in a polyphonic musical signal. A support vector model, based on a large feature set, is employed to discriminate accompanied singing voice from pure instrumental regions. We propose a temporal smoothing of the posterior probabilities with a hidden Markov model that helps adapting the segmentation sequence to the precision of the manual annotation. Quantitative results on a copyright-free public musical corpus show a classification accuracy of 82\%.",
keywords = "Hidden Markov Models, Support Vector Machines, Vocal detection",
author = "Mathieu Ramona and G. Richard and B. David",
year = "2008",
month = sep,
day = "16",
doi = "10.1109/ICASSP.2008.4518002",
language = "English",
isbn = "1424414849",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "1885--1888",
booktitle = "2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP",
note = "2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP ; Conference date: 31-03-2008 Through 04-04-2008",
}