Joint-Diagonalizability-Constrained Multichannel Nonnegative Matrix Factorization Based on Multivariate Complex Student's t-distribution

In this paper, we propose the model generalization of a fast version of multichannel nonnegative matrix factorization (FastMNMF). FastMNMF is a blind source separation (BSS) method under the assumption that the spatial covariance matrices of multiple sources are jointly diagonalizable. To further improve its source-separation performance, we introduce a multivariate complex Student's t-distribution as a generative model, which includes a multivariate complex Gaussian distribution used in conventional FastMNMF. We derive a new parameter update rule using the auxiliary-function-based method and show the validity of the proposed method on the basis of BSS experiments using music sources.