A statistical approach to musical genre classification using Non-negative Matrix Factorization

This paper introduces a new feature set based on a Non-negtive Matrix Factorization approach for the classification of musical signals into genres, only using synchronous organization of music events (vertical dimension of music). This feature set generates a vector space to describe the spectrogram representation of a music Signal. The space is modeled statistically by a mixture of Gaussians (GMM). A new signal is classified by considering the likelihoods over all the estimated feature vectors given these statistical models, without constructing a model for the signal itself. Cross-validation tests on two commonly utilized datasets for this task show the superiority of the proposed features compared to the widely used MFCC type of representation based on classification accuracies (over 9% of improvement), as well as on a stability measure introduced in this paper for GMM.QC 20161031