Independent Low-Rank Matrix Analysis Based on Time-Variant Sub-Gaussian Source Model

被引：0

作者：

Mogami, Shinichi ^{[1
]}

Takamune, Norihiro ^{[1
]}

Kitamura, Daichi ^{[2
]}

Saruwatari, Hiroshi ^{[1
]}

Takahashi, Yu ^{[3
]}

Kondo, Kazunobu ^{[3
]}

Nakajima, Hiroaki ^{[3
]}

Ono, Nobutaka ^{[4
]}

机构：

[1] Univ Tokyo, Tokyo, Japan

[2] Kagawa Coll, Natl Inst Technol, Kagawa, Japan

[3] Yamaha Corp, Shizuoka, Japan

[4] Tokyo Metropolitan Univ, Tokyo, Japan

来源：

2018 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC) | 2018年

关键词：

PERMUTATION PROBLEM; SEPARATION; FACTORIZATION; MIXTURES; ICA;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：1684 / 1691

页数：8

共 23 条

[1]

[Anonymous], EURASIP J ADV SIGN P

[2]

Araki Shoko, 2012, Latent Variable Analysis and Signal Separation. Proceedings 10th International Conference, LVA/ICA 2012, P414, DOI 10.1007/978-3-642-28551-6_51

[3] INDEPENDENT COMPONENT ANALYSIS, A NEW CONCEPT [J].