Musical Genre Classification VIA Generalized Gaussian and Alpha-Stable Modeling
Christos Tzagkarakis, Athanasios Mouchtaris, Panagiotis Tsakalides · 2006
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