Music genre classification via sparse representations of auditory temporal modulations

A robust music genre classification framework is proposed that combines the rich, psycho-physiologically grounded properties of slow temporal modulations of music recordings and the power of sparse representation-based classifiers. Linear subspace dimensionality reduction techniques are shown to play a crucial role within the framework under study. The proposed method yields a music genre classification accuracy of 91% and 93.56% on the GTZAN and the ISMIR2004 Genre dataset, respectively. Both accuracies outperform any reported accuracy ever obtained by state of the art music genre classification algorithms in the aforementioned datasets.