Perceptually enhanced blind single-channel music source separation by Non-negative Matrix Factorization
We propose a new approach that improves perceptual quality of the separated sources in blind single-channel musical source separation. It uses the advantages of subspace learning based on Non-negative Matrix Factorization (NMF) in which the bases represent the notes. The cost function is formulated in the form of weighted ?-divergence by adopting the PEAQ auditory model defined in ITU-R BS.1387 into the source separation. The proposed perceptually weighted factorization scheme is integrated into the Non-negative Matrix Factor 2-D Deconvolution (NMF2D) and Clustered Non-negative Matrix Factorization (CNMF) to overcome the source clustering problem encountered in under-determined source separation. It is shown that the introduced perceptually weighted NMF schemes, named as PW-NMF2D and PW-CNMF, efficiently learn the bases that enable us to apply a simple resynthesis of the musical sources based on the temporal model stored in the encoding matrix. Source separation performance has been reported on musical mixtures where 12 dB improvement is achieved in terms of SDR, SIR and SAR compared to the state-of-the-art methods. Performance has also been evaluated by perceptual measures resulting an improvement of 25 in OPS, TPS, IPS and APS values.
non-negative matrix factorization perceptual quality clustering blind audio source separation