In this work we improve accuracy of MFCC-based genre classification by using the Harmonic-Percussion Signal Separation (HPSS) algorithm on the music signal, and then calculate the MFCCs on the separated signals. The choice of the HPSS algorithm was mainly based on the observation that the presence of harmonics causes the high MFCCs to be noisy. A multivariate autoregressive (MAR) model was trained on the improved MFCCs, and performance in the task of genre classification was evaluated. By combining features calculated on the separated signals, relative error rate reductions of 20% and 16.2% were obtained when an SVM classifier was trained on the MFCCs and MAR features respectively. Next, by analyzing the MAR features calculated on the separated signals, it was concluded that the original signal contained some information which the MAR model was capable of handling, and that the best performance was obtained when all three signals were used. Finally, by choosing the number of MFCCs from each signal type to be used in the autoregressive modelling, it was verified that the best performance was reached when the high MFCCs calculated on the harmonic signal were discarded.
['Halfdan Rump', 'Shigeki Miyabe', 'Emiru Tsunoo', 'Nobutaka Ono', 'Shigeki Sagayama']
Autoregressive MFCC Models for Genre Classification Improved by Harmonic-percussion Separation.