Classification of musical genre: a machine learning approach.

Abstract

In this paper, we investigate the impact of machine learning algorithms in the development of automatic music classification models aiming to capture genres distinctions. The study of genres as bodies of musical items aggregated according to subjective and local criteria requires corresponding inductive models of such a notion. This process can be thus modeled as an example-driven learning task. We investigated the impact of different musical features on the inductive accuracy by first creating a medium-sized collection of examples for widely recognized genres and then evaluating the performances of different learning algorithms. In this work, features are derived from the MIDI transcriptions of the song collection.