Are tags better than audio features? The effect of joint use of tags and audio content features for artistic style clustering

Dingding Wang, Tao Li, Mitsunori Ogihara

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

17 Scopus citations

Abstract

Social tags are receiving growing interests in information retrieval. In music information retrieval previous research has demonstrated that tags can assist in music classification and clustering. This paper studies the problem of combining tags and audio contents for artistic style clustering. After studying the effectiveness of using tags and audio contents separately for clustering, this paper proposes a novel language model that makes use of both data sources. Experiments with various methods for combining feature sets demonstrate that tag features are more useful than audio content features for style clustering and that the proposed model can marginally improve clustering performance by combing tags and audio contents.

Original languageEnglish (US)
Title of host publicationProceedings of the 11th International Society for Music Information Retrieval Conference, ISMIR 2010
Pages57-62
Number of pages6
StatePublished - 2010
Event11th International Society for Music Information Retrieval Conference, ISMIR 2010 - Utrecht, Netherlands
Duration: Aug 9 2010Aug 13 2010

Publication series

NameProceedings of the 11th International Society for Music Information Retrieval Conference, ISMIR 2010

Other

Other11th International Society for Music Information Retrieval Conference, ISMIR 2010
CountryNetherlands
CityUtrecht
Period8/9/108/13/10

ASJC Scopus subject areas

  • Music
  • Information Systems

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    Wang, D., Li, T., & Ogihara, M. (2010). Are tags better than audio features? The effect of joint use of tags and audio content features for artistic style clustering. In Proceedings of the 11th International Society for Music Information Retrieval Conference, ISMIR 2010 (pp. 57-62). (Proceedings of the 11th International Society for Music Information Retrieval Conference, ISMIR 2010).