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This work addresses the problem of multichannel source separation combiningtwo powerful approaches, multichannel spectral factorization with recentmonophonic deep-learning (DL) based spectrum inference. Individual sourcespectra at different channels are estimated with a Masker-Denoiser Twin Network(MaD TwinNet), able to model long-term temporal patterns of a musical piece.The monophonic source spectrograms are used within a spatial covariance mixingmodel based on Complex Non-Negative Matrix Factorization (CNMF) that predictsthe spatial characteristics of each source. The proposed framework is evaluatedon the task of singing voice separation with a large multichannel dataset.Experimental results show that our joint DL+CNMF method outperforms both theindividual monophonic DL-based separation and the multichannel CNMF baselinemethods.

Multichannel Singing Voice Separation by Deep Neural Network Informed DOA Constrained CNMF