Title :
Two-stage neural network for blind sources separation
Author :
Choi, Seungjin ; Liu, Ruey- Wen
Author_Institution :
Dept. of Electr. Eng., Notre Dame Univ., IN, USA
Abstract :
In this paper, an on-line implementation of the simultaneous diagonalization (SD) of two different symmetric matrices is addressed. A two-stage neural network which consists of self-normalizing decorrelation and extended Oja´s rule, is presented for an on-line implementation of SD. The SD of the 2nd- and 4th-order moment matrices is known as one solution to the blind sources separation problem. It will be shown that the two-stage network presented can recover the source signals from a linear mixture without the knowledge of the mixing matrix and the distribution of the source signals
Keywords :
correlation methods; higher order statistics; matrix algebra; neural nets; signal processing; blind sources separation; extended Oja´s rule; fourth-order moment matrices; linear mixture; moment matrices; on-line implementation; second-order moment matrices; self-normalizing decorrelation; simultaneous diagonalization; symmetric matrices; two-stage neural network; Covariance matrix; Decorrelation; Higher order statistics; Intelligent networks; Laboratories; Neural networks; Signal analysis; Symmetric matrices; Vectors; Visual perception;
Conference_Titel :
Circuits and Systems, 1996., IEEE 39th Midwest symposium on
Conference_Location :
Ames, IA
Print_ISBN :
0-7803-3636-4
DOI :
10.1109/MWSCAS.1996.588042
