Independent Low-Rank Matrix Analysis Based on Time-Variant Sub-Gaussian Source Model for Determined Blind Source Separation

摘要

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 majorization-equalization 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.