Music Genre Recognition Using Gabor Filters and LPQ Texture Descriptors

This paper presents a novel approach for automatic music genre recognition in the visual domain that uses two texture descriptors. For this, the audio signal is converted into spectrograms and then textural features are extracted from this visual representation. Gabor filters and LPQ texture descriptors were used to capture the spectrogram content. In order to evaluate the performance of local feature extraction, some different zoning mechanisms were taken into account. The experiments were performed on the Latin Music Database. At the end, we have shown that the SVM classifier trained with LPQ is able to achieve a recognition rate above 80%. This rate is among the best results ever presented in the literature.