Boosting classifiers for music genre classification

Ulaş Baǧci*, Engin Erzin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations


Music genre classification is an essential tool for music information retrieval systems and it has been finding critical applications in various media platforms. Two important problems of the automatic music genre classification are feature extraction and classifier design. This paper investigates discriminative boosting of classifiers to improve the automatic music genre classification performance. Two classifier structures, boosting of the Gaussian mixture model based classifiers and classifiers that are using the inter-genre similarity information, are proposed. The first classifier structure presents a novel extension to the maximum-likelihood based training of the Gaussian mixtures to integrate GMM classifier into boosting architecture. In the second classifier structure, the boosting idea is modified to better model the inter-genre similarity information over the mis-classified feature population. Once the intergenre similarities are modeled, elimination of the inter-genre similarities reduces the inter-genre confusion and improves the identification rates. A hierarchical auto-clustering classifier scheme is integrated into the intergenre similarity modeling. Experimental results with promising classification improvements are provided.

Original languageEnglish (US)
Title of host publicationComputer and Information Sciences - ISCIS 2005 - 20th International Symposium, Proceedings
Number of pages10
StatePublished - 2005
Externally publishedYes
Event20th International Symposium on Computer and Information Sciences, ISCIS 2005 - Istanbul, Turkey
Duration: Oct 26 2005Oct 28 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3733 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th International Symposium on Computer and Information Sciences, ISCIS 2005

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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