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

  • Meng A
  • Shawe-Taylor J
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Abstract

In music genre classification the decision time is typically of\r
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\r
autoregressive model for modelling short time features.\r
Furthermore, it was investigated how these models can be\r
integrated over a segment of short time features into a kernel\r
such that a support vector machine can be applied. Two kernels\r
with this property were considered, the convolution kernel and product probability kernel.\r
In order to examine the different methods an 11 genre music\r
setup was utilized. In this setup the Mel Frequency Cepstral\r
Coefficients (MFCC) were used as short time features. The\r
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.

Author-supplied keywords

  • Computational
  • Information-Theoretic Learning with
  • Learning/Statistics & Optimisation
  • convolution kernel
  • feature integration
  • kernel
  • product probability
  • support vector machine

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Authors

  • A. Meng

  • J. Shawe-Taylor

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