Co-occurrence Models in Music Genre Classification
Publication: Research - peer-review › Article in proceedings – Annual report year: 2005
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Co-occurrence Models in Music Genre Classification. / Ahrendt, Peter; Goutte, Cyril; Larsen, Jan.
In: IEEE International workshop on Machine Learning for Signal Processing. IEEE, 2005. p. 247-252.Publication: Research - peer-review › Article in proceedings – Annual report year: 2005
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TY - GEN
T1 - Co-occurrence Models in Music Genre Classification
A1 - Ahrendt,Peter
A1 - Goutte,Cyril
A1 - Larsen,Jan
AU - Ahrendt,Peter
AU - Goutte,Cyril
AU - Larsen,Jan
PB - IEEE
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
KW - probabilistic models
KW - music genre classification
U2 - 10.1109/MLSP.2005.1532908
DO - 10.1109/MLSP.2005.1532908
SN - 0-7803-9517-4
BT - IEEE International workshop on Machine Learning for Signal Processing
T2 - IEEE International workshop on Machine Learning for Signal Processing
SP - 247
EP - 252
ER -