Evaluation of Distance Measures Between Gaussian Mixture Models of MFCCs

Jesper Højvang Jensen, Dan P. W. Ellis, Mads Græsbøll Christensen, Søren Holdt Jensen

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

41 Citations (Scopus)
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In music similarity and in the related task of genre classification, a distance measure between Gaussian mixture models is frequently needed. We present a comparison of the Kullback-Leibler distance, the earth movers distance and the normalized L2 distance for this application. Although the normalized L2 distance was slightly inferior to the Kullback-Leibler distance with respect to classification performance, it has the advantage of obeying the triangle inequality, which allows for efficient searching.
Original languageEnglish
Title of host publicationProceedings of the 8th International Conference on Music Information Retrieval
Number of pages2
PublisherAustrian Computer Society
Publication date2007
ISBN (Print)978-3-85403-218-2
Publication statusPublished - 2007
EventInternational Conference on Music Information Retrieval - Vienna, Austria
Duration: 23 Sep 200727 Sep 2007
Conference number: 8


ConferenceInternational Conference on Music Information Retrieval


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  • IntelligentSound

    Jensen, S. H., Christensen, M. G., Jensen, J. H. & Laurberg, H.



    Project: Research

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