High--Performance music information retrieval system for song genre classification

Authors: Schierz, A.C. and Budka, M.

Editors: Kryszkiewicz, M., Rybinski, H., Skowron, A. and Ras, Z.W.

Conference: Proc. of the 19th International Symposium on Methodologies for Intelligent Systems (ISMIS'11)

Dates: June 2011

Pages: 725-733

Publisher: Springer-Verlag

Place of Publication: Lecture Notes in Computer Science, 2011, Volume 6804

DOI: 10.1007/978-3-642-21916-0_76


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%.


Source: Manual

Preferred by: Marcin Budka