Music genre recognition using spectrograms with harmonic-percussive sound separation

In this work we assesses the music genre classification using spectrograms taken from the original signal, percussive content signal, and harmonic content signal. The rationale behind this is that classifiers obtained from this three different representation of the signal may present some complementarity to each other. By this way, one can improve the recognition rates already obtained in previous works which has explored only the original signal content. LBP texture features were used to represent the spectrogram content, and the classification step was supported by SVM. The spectrogram images were zoned taking to account a perceptual scale, and a specific classifier was created for each zone, which has led us to combine classifiers outputs to get the final decision. The performance of our approach reaches the recognition rate about 88.56% which, to the best of our knowledge, is the best rate ever obtained on the LMD dataset using artist filter constraint.