Song-Level Features and Support Vector Machines for Music Classification

Abstract

Searching and organizing growing digital music collections requires automatic classification of music. This paper describes a new system, tested on the task of artist identification, that uses support vector machines to classify songs based on features calculated over their entire lengths. Since support vector machines are exemplar-based classifiers, training on and classifying entire songs instead of short-time features makes intuitive sense. On a dataset of 1200 pop songs performed by 18 artists, we show that this classifier outperforms similar classifiers that use only SVMs or song-level features. We also show that the KL divergence between single Gaussians and Mahalanobis distance between MFCC statistics vectors perform comparably when classifiers are trained and tested on separate albums, but KL divergence outperforms Mahalanobis distance when trained and tested on songs from the same albums.