Co-occurrence Models in Music Genre Classification

Peter Ahrendt, Cyril Goutte, Jan Larsen

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    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
    Publication date2005
    ISBN (Print)0-7803-9517-4
    Publication statusPublished - 2005
    Event2005 IEEE International Workshop on Machine Learning for Signal Processing - Mystic, CT, United States
    Duration: 28 Sep 200530 Sep 2005


    Workshop2005 IEEE International Workshop on Machine Learning for Signal Processing
    CountryUnited States
    CityMystic, CT
    Internet address


    • probabilistic models
    • music genre classification

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