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Proceedings Paper

Music genre classification via likelihood fusion from multiple feature models
Author(s): Yu Shiu; C.-C. Jay Kuo
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Paper Abstract

Music genre provides an efficient way to index songs in a music database, and can be used as an effective means to retrieval music of a similar type, i.e. content-based music retrieval. A new two-stage scheme for music genre classification is proposed in this work. At the first stage, we examine a couple of different features, construct their corresponding parametric models (e.g. GMM and HMM) and compute their likelihood functions to yield soft classification results. In particular, the timbre, rhythm and temporal variation features are considered. Then, at the second stage, these soft classification results are integrated to result in a hard decision for final music genre classification. Experimental results are given to demonstrate the performance of the proposed scheme.

Paper Details

Date Published: 17 January 2005
PDF: 11 pages
Proc. SPIE 5682, Storage and Retrieval Methods and Applications for Multimedia 2005, (17 January 2005);
Show Author Affiliations
Yu Shiu, Univ. of Southern California (United States)
C.-C. Jay Kuo, Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 5682:
Storage and Retrieval Methods and Applications for Multimedia 2005
Rainer W. Lienhart; Noboru Babaguchi; Edward Y. Chang, Editor(s)

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