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

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.

• Type: Conference
• Year: 2005
• Where: ISMIR
• Authors: Anders Meng, John Shawe-Taylor
• Keywords: Information Retrieval, ISMIR 2005, Multivariate Autoregressive Model, Music Genre Classification, Short Time Features