On local convergence of a class of blind separation algorithms

Author

Lindgren, Ulf ; Wigren, Torbjöm ; Broman, Holger

Author_Institution

Dept. of Appl. Electron, Chalmers Univ. of Technol., Goteborg, Sweden

Volume

43

Issue

12

fYear

1995

fDate

12/1/1995 12:00:00 AM

Firstpage

3054

Lastpage

3058

Abstract

A class of recursive stochastic gradient algorithms for blind separation of dynamically mixed independent source signals are analyzed. The studied methods utilize correlations and high-order moments in order to enforce statistical independence of the separated signals. The local convergence properties of the schemes are investigated, and it is demonstrated that local convergence is tied to positive realness of certain mixing transfer functions

Keywords

convergence of numerical methods; correlation methods; higher order statistics; recursive estimation; signal processing; stochastic processes; transfer functions; blind separation algorithms; correlations; dynamically mixed independent source signals; high-order moments; local convergence properties; mixing transfer functions; recursive stochastic gradient algorithms; separated signals; statistical independence; Algorithm design and analysis; Convergence; Crosstalk; Microphones; Nonlinear filters; Signal analysis; Signal processing; Signal processing algorithms; Stochastic processes; Transfer functions;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.476456

Filename

476456