Title :
Musical Genre Classification VIA Generalized Gaussian and Alpha-Stable Modeling
Author :
Tzagkarakis, C. ; Mouchtaris, A. ; Tsakalides, P.
Author_Institution :
Dept. of Comput. Sci., Crete Univ.
Abstract :
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
Keywords :
Gaussian processes; audio signal processing; feature extraction; music; signal classification; statistical analysis; wavelet transforms; Kullback-Leibler divergence; alpha-stable modeling; feature extraction; generalized Gaussian density; musical genre classification; statistical similarity measurement approach; wavelet decomposition; wavelet distributions; Computer science; Feature extraction; Iron; Multimedia databases; Multiple signal classification; Music information retrieval; Parameter estimation; Samarium; Wavelet coefficients; Wavelet transforms;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on
Conference_Location :
Toulouse
Print_ISBN :
1-4244-0469-X
DOI :
10.1109/ICASSP.2006.1661251