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In this paper, we address a convolutive blind source separation (BSS) problemand propose a new extended framework of FastMNMF by introducing priorinformation for joint diagonalization of the spatial covariance matrix model.Recently, FastMNMF has been proposed as a fast version of multichannelnonnegative matrix factorization under the assumption that the spatialcovariance matrices of multiple sources can be jointly diagonalized. However,its source-separation performance was not improved and the physical meaning ofthe joint-diagonalization process was unclear. To resolve these problems, wefirst reveal a close relationship between the joint-diagonalization process andthe demixing system used in independent low-rank matrix analysis (ILRMA). Next,motivated by this fact, we propose a new regularized FastMNMF supported byILRMA and derive convergence-guaranteed parameter update rules. From BSSexperiments, we show that the proposed method outperforms the conventionalFastMNMF in source-separation accuracy with almost the same computation time.

Regularized Fast Multichannel Nonnegative Matrix Factorization with ILRMA-based Prior Distribution of Joint-Diagonalization Process