An Investigation of Feature Models for Music Genre Classification using the Support Vector Classifier

Anders Meng, John Shawe-Taylor

AbstractIn music genre classification the decision time is typically of
the order of several seconds however most automatic music genre
classification systems focus on short time features derived from
10-50ms. This work investigates two models, the multivariate gaussian model and the multivariate autoregressive model for modelling short time features.
Furthermore, it was investigated how these models can be
integrated over a segment of short time features into a kernel
such that a support vector machine can be applied. Two kernels
with this property were considered, the convolution kernel
and product probability kernel.

In order to examine the different methods an 11 genre music
setup was utilized. In this setup the Mel Frequency Cepstral
Coefficients (MFCC) were used as short time features. The
accuracy of the best performing model on this data set was 44% as compared to a human performance of 52% on the same data set.
KeywordsFeature Integration, Product Probability Kernel, Convolution Kernel, Support Vector Machine, Music Genre
TypeConference paper [With referee]
ConferenceInternational Conference on Music Information Retrieval
Year2005    Month September    pp. 604-609
NoteFinal version : 6 pages instead of original 8 due to poster presentation.
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing

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