@ARTICLE{Rosner_Aldona_Classification_2014, author={Rosner, Aldona and Kostek, Bożena and Schuller, Bjӧrn}, volume={vol. 39}, number={No 4}, journal={Archives of Acoustics}, pages={629-638}, howpublished={online}, year={2014}, publisher={Polish Academy of Sciences, Institute of Fundamental Technological Research, Committee on Acoustics}, abstract={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.}, type={Artykuły / Articles}, title={Classification of Music Genres Based on Music Separation into Harmonic and Drum Components}, URL={http://journals.pan.pl/Content/101491/PDF/22_paper.pdf}, doi={10.2478/aoa-2014-0068}, keywords={Music Information Retrieval, musical sound separation, drum separation, music genre classification, support vector machine, co-training, Non-Negative Matrix Factorization}, }