Set of texture descriptors for music genre classification
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 sub windows 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.
Information Technology and Cybersecurity
Image processing, Music genre, Pattern recognition, Texture
Nanni, Loris, Yandre Costa, and Sheryl Brahnam. “Set of Texture Descriptors for Music Genre Classification.” In proceeding of the 22nd WSCG International Conference on Computer Graphics, Visualization and Computer Vision, Plzen, Czech Republic (2014)
22nd International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, WSCG 2014, Communication Papers Proceedings - in co-operation with EUROGRAPHICS Association