Automatic music genre classification based on wavelet package transform and best basis algorithm

In this paper, an improved music genre classification method is presented. The proposed method makes use of the wavelet package transform (WPT) and the best basis algorithm (BBA) to accurately classify and increase classification performance. It is well known that WPT can generate a wavelet decomposition that offers a richer signal analysis. In this paper, the music signal is first decomposed into approximation and detail coefficients using WPT with the best basis algorithm to minimize the Shannon entropy and maximize the representation of music signal. This paper uses the Top-Down search strategy with cost function to select the best basis. Then the proposed method could apply support vector machine (SVM) to build a music genre classifier using the mel-frequency cepstral coefficients (MFCC) and log energies extracted from the decomposition coefficients of WPT with the best basis algorithm. Finally one can perform music genre classification with the built music genre classifier. Experiments conducted on three different music datasets have shown that the proposed method can achieve higher classification accuracy than other music genre classification methods with the same experimental setup.