Co-occurrence Models in Music Genre Classification

Peter Ahrendt, Cyril Goutte, Jan Larsen

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    Abstract

    Music genre classification has been investigated using many different methods, but most of them build on probabilistic models of feature vectors x\_r which only represent the short time segment with index r of the song. Here, three different co-occurrence models are proposed which instead consider the whole song as an integrated part of the probabilistic model. This was achieved by considering a song as a set of independent co-occurrences (s, x\_r) (s is the song index) instead of just a set of independent (x\_r)'s. The models were tested against two baseline classification methods on a difficult 11 genre data set with a variety of modern music. The basis was a so-called AR feature representation of the music. Besides the benefit of having proper probabilistic models of the whole song, the lowest classification test errors were found using one of the proposed models.
    Original languageEnglish
    Title of host publicationIEEE International workshop on Machine Learning for Signal Processing
    PublisherIEEE
    Publication date2005
    Pages247-252
    ISBN (Print)0-7803-9517-4
    DOIs
    Publication statusPublished - 2005
    Event2005 IEEE International Workshop on Machine Learning for Signal Processing - Mystic, CT, United States
    Duration: 28 Sep 200530 Sep 2005
    http://mlsp2005.conwiz.dk/

    Workshop

    Workshop2005 IEEE International Workshop on Machine Learning for Signal Processing
    CountryUnited States
    CityMystic, CT
    Period28/09/200530/09/2005
    Internet address

    Keywords

    • probabilistic models
    • music genre classification

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