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Independent low-rank matrix analysis (ILRMA) is a fast and stable method of blind audio source separation. Conventional ILRMAs assume time-variant (super-)Gaussian source models, which can only represent signals that follow a super-Gaussian distribution. In this article, we focus on ILRMA based on a generalized Gaussian distribution (GGD-ILRMA) and propose a new type of GGD-ILRMA that adopts a time-variant sub-Gaussian distribution for the source model. We propose a new update scheme called generalized iterative projection for homogeneous source models (GIP-HSM) and obtain a convergence-guaranteed update rule for demixing spatial parameters by combining the GIP-HSM scheme and the majorization-minimization (MM) algorithm. Furthermore, a new extension of the MM algorithm is proposed for the convergence acceleration by applying the majorizationequalization algorithm to a multivariate case. In the experimental evaluation, we show the versatility of the proposed method, i.e., the proposed time-variant sub-Gaussian source model can be applied to various types of source signal. Index Terms-Blind source separation, independent low-rank matrix analysis (ILRMA), generalized Gaussian distribution. I. INTRODUCTION B LIND source separation (BSS) [1]-[14] is a technique of extracting specific sources from an observed multichannel mixture signal without knowing a priori information about the mixing system. Independent component analysis (ICA) and its extensions, such as independent vector analysis (IVA) [5], [6], are the most commonly used approaches for BSS in an

In this letter, we address monaural source separation based on supervised nonnegative matrix factorization (SNMF) and propose a new penalized SNMF. Conventional SNMF often degrades the separation performance owing to the basis-sharing problem. Our penalized SNMF forces nontarget bases to become different from the target bases, which increases the separated sound quality.

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