Vocal Segment Classification in Popular Music

This paper explores the vocal and non-vocal music classification problem within popular songs. A newly built labeled database covering 147 popular songs is announced. It is designed for classifying signals from 1sec time windows. Features are selected for this particular task, in order to capture both the temporal correlations and the dependencies among the feature dimensions. We systematically study the performance of a set of classifiers, including linear regression, generalized linear model, Gaussian mixture model, reduced kernel orthonormalized partial least squares and K-means on cross-validated training and test setup. The database is divided in two different ways: with/without artist overlap between training and test sets, so as to study the so called ‘artist effect’. The performance and results are analyzed in depth: from error rates to sample-to-sample error correlation. A voting scheme is proposed to enhance the performance under certain conditions.