Projective non-negative matrix factorization with Bregman divergence for musical instrument classification

Abstract

In this paper, the projective non-negative matrix factorization (PNMF) with Bregman divergence is applied into the musical instrument classification. A novel supervised learning algorithm for automatic classification of individual musical instrument sounds is addressed inspiring from PNMF with several versions of Bregman divergence. Moreover, the orthogonality of basis matrices between PNMF and conventional non-negative matrix factorization (NMF) is compared. In addition, three classifiers based on nearest neighbors (NN), Gaussian mixture model (GMM) and radial basis function (RBF) are added to evaluate the performance of PNMF classifier. The results indicate that the classification accuracy of the proposed PNMF classifier outperforms the classifiers derived from conventional NMF and machine learning.