High-performance music information retrieval system for song genre classification
Authors: Schierz, A. and Budka, M.
Volume: 6804 LNAI
Pages: 725-733
ISBN: 9783642219153
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