Music genre classification with taxonomy

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

46 Scopus citations


Automatic music genre classification is a fundamental component of music information retrieval systems and has been gaining importance and enjoying a growing amount of attention with the emergence of digital music on the Internet. Although considerable research has been conducted in automatic music genre classification, little has been done on hierarchical classification with taxonomies. The underlying hierarchical taxonomy identifies the relationships of dependence between different genres and provides valuable sources of information for genre classification. This paper investigates the use of taxonomy for music genre classification. Our empirical experiments on two datasets show that using taxonomy improves the classification performance. We also propose an approach for automatically generating genre taxonomies based on the confusion matrix via linear discriminant projection. Our work also provides some insights for future research.

Original languageEnglish (US)
Title of host publication2005 IEEE ICASSP '05 - Proc. - Design and Implementation of Signal Proces.Syst.,Indust. Technol. Track,Machine Learning for Signal Proces. Education, Spec. Sessions
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)0780388747, 9780780388741
StatePublished - Jan 1 2005
Externally publishedYes
Event2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Philadelphia, PA, United States
Duration: Mar 18 2005Mar 23 2005

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149


Other2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
Country/TerritoryUnited States
CityPhiladelphia, PA

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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