AN INVESTIGATION OF FEATURE MODELS FOR MUSIC GENRE
CLASSIFICATION USING THE SUPPORT VECTOR CLASSIFIER
John Shawe-Taylor and Anders Meng
In: ISMIR 2005, 6th International Conference on Music Information Retrieval, 11 - 15 September 2005, London, UK.
In 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 were used as short time features. The accuracy of the best performing model on this data set was 44% compared to a human performance of 52% on the same data set.