Independent Low-Rank Matrix Analysis Based on Time-Variant Sub-Gaussian Source Model
Abstract
Independent low-rank matrix analysis (ILRMA) is a fast and stable method for 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 paper, 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. By using a new update scheme called generalized iterative projection for homogeneous source models, we obtain a convergence-guaranteed update rule for demixing spatial parameters. 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.
Keywords
Cite
@article{arxiv.1808.08056,
title = {Independent Low-Rank Matrix Analysis Based on Time-Variant Sub-Gaussian Source Model},
author = {Shinichi Mogami and Norihiro Takamune and Daichi Kitamura and Hiroshi Saruwatari and Yu Takahashi and Kazunobu Kondo and Hiroaki Nakajima and Nobutaka Ono},
journal= {arXiv preprint arXiv:1808.08056},
year = {2018}
}
Comments
8 pages, 5 figures, To appear in the Proceedings of APSIPA ASC 2018