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.
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