A statistically sparse decomposition principle for underdetermined blind source separation

Author

Xiao, Ming ; Xie, Shengli ; Fu, Yuli

fYear

2005

fDate

13-16 Dec. 2005

Firstpage

165

Lastpage

168

Abstract

The underdetermined case in blind source separation, that is, separation of n sources from m (m1 -norm solution. Second, we present a new sparse representation based on second order statistic, which is called statistically sparse decomposition principle (SSDP). Finally, speech signal experiments demonstrate the performance of the approach.

Keywords

blind source separation; speech processing; statistical analysis; blind source separation; second order statistic; sparse representation; statistically sparse decomposition principle; Blind source separation; Educational institutions; Independent component analysis; Laplace equations; Matrix decomposition; Noise robustness; Source separation; Sparse matrices; Speech; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Signal Processing and Communication Systems, 2005. ISPACS 2005. Proceedings of 2005 International Symposium on

Print_ISBN

0-7803-9266-3

Type

conf

DOI

10.1109/ISPACS.2005.1595372

Filename

1595372

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3272943