Peter Ahrendt, Cyril Goutte, Jan Larsen
IEEE International workshop on Machine Learning for Signal Processing, Mystic, Connecticut, USA, September 28-30, 2005.
Music genre classification has been investigated using
many different methods, but most of them build on probabilistic
models of feature vectors xr 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; xr) (s is the song
index) instead of just a set of independent (xr) 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.
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