High-performance music information retrieval system for song genre classification

Authors:
Amanda Schierz;Marcin Budka
Affiliations:
Smart Technology Research Centre, School of Design, Engineering and Computing, Bournemouth University, Poole, United Kingdom;Smart Technology Research Centre, School of Design, Engineering and Computing, Bournemouth University, Poole, United Kingdom
Venue:
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Year:
2011

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Abstract

With the large amounts of multimedia data produced, recorded and made available every day, there is a clear need for well-performing automatic indexing and search methods. This paper describes a music genre classification system, which was a winning solution in the Music Information Retrieval ISMIS 2011 contest. The system consisted of a powerful ensemble classifier using the Error Correcting Output Coding coupled with an original, multi-resolution clustering and iterative relabelling scheme. The two approaches used together outperformed other competing solutions by a large margin, reaching the final accuracy close to 88%.