Comparing textural features for music genre classification

In this paper we compare two different textural feature sets for automatic music genre classification. The idea is to convert the audio signal into spectrograms and then extract features from this visual representation. Two textural descriptors are explored in this work: the Gray Level Co-Occurrence Matrix (GLCM) and Local Binary Patterns (LBP). Besides, two different strategies of extracting features are considered: a global approach where the features are extracted from the entire spectrogram image and then classified by a single classifier; a local approach where the spectrogram image is split into several zones which are classified independently and final decision is then obtained by combining all the partial results. The database used in our experiments was the Latin Music Database, which contains music pieces categorized into 10 musical genres, and has been used for MIREX (Music Information Retrieval Evaluation eXchange) competitions. After a comprehensive series of experiments we show that the SVM classifier trained with LBP is able to achieve a recognition rate of 80%. This rate not only outperforms the GLCM by a fair margin but also is slightly better than the results reported in the literature.