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

  • Meng A
  • Shawe-Taylor J
  • 1


    Mendeley users who have this article in their library.
  • N/A


    Citations of this article.


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

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • A. Meng

  • J. Shawe-Taylor

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free