Perceptually weighted Non-negative Matrix Factorization for blind single-channel music source separation

Abstract

We propose a blind single-channel musical source separation method that improves perceptual quality of the separated sources. It uses the advantages of subspace learning based on Non-negative Matrix Factor 2-D Deconvolution (NMF2D). To improve the perceptual quality of separation, we propose a weighted divergence type cost function for the optimization that… (More)

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