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@conference{805c1d51bf794ed7b3cb60da3d329a78, author = " Antonio-Jes{\'u}s Mu{\~n}oz-Montoro and {Julio J.} Carabias-Orti and Archontis Politis and Konstantinos Drossos", abstract = "This work addresses the problem of multichannel source separation combining two powerful approaches, multichannel spectral factorization with recent monophonic deep-learning (DL) based spectrum inference. Individual source spectra 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 mixing model based on Complex Non-Negative Matrix Factorization (CNMF) that predicts the spatial characteristics of each source. The proposed framework is evaluated on the task of singing voice separation with a large multichannel dataset. Experimental results show that our joint DL+CNMF method outperforms both the individual monophonic DL-based separation and the multichannel CNMF baseline methods.", booktitle = "IEEE International Workshop on Multimedia Signal Processing (MMSP)", title = "{M}ultichannel {S}inging {V}oice {S}eparation by {D}eep {N}eural {N}etwork {I}nformed {DOA} {C}onstrained {CNMF}", year = "2020", }