On mixture model complexity estimation for music recommender systems

W. Balkema, Ferdinand van der Heijden, B. Meijerink

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


    Content-based music navigation systems are in need of robust music similarity measures. Current similarity measures model each song with the same model parameters. We propose methods to efficiently estimate the required number of model parameters of each individual song. First results of a study on relationships between a small set of basic audio features are presented. We conclude that there are only very small correlations between models on low- and on high-dimensional features. When we compare a very simple clustering algorithm with an algorithm that estimates model parameters using the MDL criterium, we find a surprisingly strong correlation between the estimated number of mixture components.
    Original languageUndefined
    Title of host publication17th Annual Workshop on Circuits
    Number of pages4
    ISBN (Print)978-90-73461-44-4
    Publication statusPublished - 23 Nov 2006
    Event17th Annual Workshop on Circuits, Systems and Signal Processing, ProRISC 2006 - Veldhoven, Netherlands
    Duration: 23 Nov 200624 Nov 2006
    Conference number: 17

    Publication series



    Workshop17th Annual Workshop on Circuits, Systems and Signal Processing, ProRISC 2006
    Abbreviated titleProRISC 2006


    • EWI-8916
    • METIS-237865
    • IR-63883

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