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In this paper, we address a statistical model extension of multichannelnonnegative matrix factorization (MNMF) for blind source separation, and wepropose a new parameter update algorithm used in the sub-Gaussian model. MNMFemploys full-rank spatial covariance matrices and can simulate situations inwhich the reverberation is strong and the sources are not point sources. Inconventional MNMF, spectrograms of observed signals are assumed to follow amultivariate Gaussian distribution. In this paper, first, to extend the MNMFmodel, we introduce the multivariate generalized Gaussian distribution as themultivariate sub-Gaussian distribution. Since the cost function of MNMF basedon this multivariate sub-Gaussian model is difficult to minimize, weadditionally introduce the joint-diagonalizability constraint in spatialcovariance matrices to MNMF similarly to FastMNMF, and transform the costfunction to the form to which we can apply the auxiliary functions to derivethe valid parameter update rules. Finally, from blind source separationexperiments, we show that the proposed method outperforms the conventionalmethods in source-separation accuracy.

Joint-Diagonalizability-Constrained Multichannel Nonnegative Matrix Factorization Based on Multivariate Complex Sub-Gaussian Distribution