An Investigation of Feature Models for Music Genre Classification using the Support Vector Classifier |
Anders Meng, John Shawe-Taylor
|
| Abstract | 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 (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. |
| Keywords | Feature Integration, Product Probability Kernel, Convolution Kernel, Support Vector Machine, Music Genre |
| Type | Conference paper [With referee] |
| Conference | International Conference on Music Information Retrieval |
| Year | 2005 Month September pp. 604-609 |
| Note | Final version : 6 pages instead of original 8 due to poster presentation. |
| Electronic version(s) | [pdf] |
| BibTeX data | [bibtex] |
| IMM Group(s) | Intelligent Signal Processing |