N2 - This article presents a study on music genre classification based on music separation into harmonic and drum components. For this purpose, audio signal separation is executed to extend the overall vector of parameters by new descriptors extracted from harmonic and/or drum music content. The study is performed using the ISMIS database of music files represented by vectors of parameters containing music features. The Support Vector Machine (SVM) classifier and co-training method adapted for the standard SVM are involved in genre classification. Also, some additional experiments are performed using reduced feature vectors, which improved the overall result. Finally, results and conclusions drawn from the study are presented, and suggestions for further work are outlined. L1 - http://www.journals.pan.pl/Content/101491/PDF/22_paper.pdf L2 - http://www.journals.pan.pl/Content/101491 PY - 2014 IS - No 4 EP - 638 DO - 10.2478/aoa-2014-0068 KW - Music Information Retrieval KW - musical sound separation KW - drum separation KW - music genre classification KW - support vector machine KW - co-training KW - Non-Negative Matrix Factorization A1 - Rosner, Aldona A1 - Kostek, Bożena A1 - Schuller, Bjӧrn PB - Polish Academy of Sciences, Institute of Fundamental Technological Research, Committee on Acoustics VL - vol. 39 DA - 2014 T1 - Classification of Music Genres Based on Music Separation into Harmonic and Drum Components SP - 629 UR - http://www.journals.pan.pl/dlibra/publication/edition/101491 T2 - Archives of Acoustics