@CONFERENCE\{IMM2005-03891,
    author       = "P. Ahrendt and C. Goutte and J. Larsen",
    title        = "Co-occurrence Models in Music Genre Classification",
    year         = "2005",
    month        = "sep",
    keywords     = "music genre classification, probabilistic models",
    pages        = "247-252",
    booktitle    = "{IEEE} International workshop on Machine Learning for Signal Processing",
    volume       = "",
    series       = "",
    editor       = "V. Calhoun, T. Adali, J. Larsen, D. Miller, S. Douglas",
    publisher    = "",
    organization = "",
    address      = "Mystic, Connecticut, {USA}",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/3891-full.html",
    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."
}