Set of Texture Descriptors for Music Genre Classifcation
This paper presents a comparison among different texture descriptors and ensembles of descriptors for music genre classification. The features are extracted from the spectrogram calculated starting from the audio signal. The best results are obtained by extracting features from subwindows taken from the entire spectrogram by Mel scale zoning. To assess the performance of our method, two different databases are used: the Latin Music Database (LMD) and the ISMIR 2004 database. The best descriptors proposed in this work greatly outperform previous results using texture descriptors on both databases: we obtain 86.1% accuracy with LMD and 82.9% accuracy with ISMIR 2004. Our descriptors and the MATLAB code for all experiments reported in this paper will be available at https://www.dei.unipd.it/node/2357.
: Music genre, texture, image processing, pattern recognition.