High-performance music information retrieval system for song genre classification

Authors: Schierz, A. and Budka, M.

Volume: 6804 LNAI

Pages: 725-733

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

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%. © 2011 Springer-Verlag Berlin Heidelberg.

Source: Scopus

High-Performance Music Information Retrieval System for Song Genre Classification

Authors: Schierz, A. and Budka, M.

Volume: 6804

Pages: 725-733

ISBN: 978-3-642-21915-3

Source: Web of Science (Lite)

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.

Publisher: Springer-Verlag

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%.

http://ismis2011.ii.pw.edu.pl/index.php

Source: Manual

Preferred by: Marcin Budka

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.

Volume: 6804

Pages: 725-733

Publisher: Springer

ISBN: 978-3-642-21915-3

https://doi.org/10.1007/978-3-642-21916-0

Source: DBLP