Musical genre classification via generalized Gaussian and alpha-stable modeling

This paper describes a novel methodology for automatic musical genre classification based on a feature extraction /statistical similarity measurement approach. First, we perform a 1-D wavelet decomposition of the music signal and we model the resulting subband coefficients using the Generalized Gaussian Density (GGD) and the Alpha-Stable distribution. Subsequently, the GGD and Alpha-Stable distribution parameters are estimated during the feature extraction step, while the similarity between two music signals is measured by employing the Kullback-Leibler Divergence (KLD) between their corresponding estimated wavelet distributions. We evaluate the performance of the proposed methodology by using a dataset consisting of six different musical genre sets.