A Wavelet Packet representation of audio signals for music genre classification using different ensemble and feature selection techniques

Authors:
Marco Grimaldi;Pádraig Cunningham;Anil Kokaram
Affiliations:
Trinity College Dublin, Dublin, Ireland;Trinity College Dublin, Dublin, Ireland;Trinity College Dublin, Dublin, Ireland
Venue:
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Year:
2003

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Abstract

The vast amount of music available electronically presents considerable challenges for information retrieval. There is a need to annotate music items with descriptors in order to facilitate retrieval. In this paper we present a process for determining the music genre of an item using a new set of descriptors. A Wavelet Packet Transform is applied to obtain the signal representation at different levels. Time and frequency features are extracted from these levels taking into account the nature of music. Using round-robin and one-against-all ensembles of simple classifiers, together with feature selection methods, we evaluate the best signal representation for music genre classification. Ensembles based on different feature sub-spaces are explored as well in order to overcome over-fitting issues. Our evaluation shows that Wavelet Packet analysis together with ensemble methods achieves very good classification accuracy.