Vocal detection in music with support vector machines

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%.