Generalized anti-Hebbian learning for source separation

Author

Wu, Hsiao-Chun ; Principe, Jose C.

Author_Institution

Dept. of Electr. & Comput. Eng., Florida Univ., Gainesville, FL, USA

Volume

2

fYear

1999

fDate

15-19 Mar 1999

Firstpage

1073

Abstract

The information-theoretic framework for source separation is highly suitable. However the choice of the nonlinearity or the estimation of the multidimensional joint probability density function are nontrivial. We propose here a generalized Gaussian model to construct a generalized blind source separation network based on the minimum entropy principle. This new separation network can suppress the interference to a significant amount compared to the traditional LMS-echo-canceler. The simulation is given to show the disparity of the performance as a varies. Finally how to choose the appropriate a in our generalized anti-Hebbian rule is discussed

Keywords

Gaussian distribution; interference suppression; knowledge based systems; learning (artificial intelligence); minimum entropy methods; signal processing; LMS-echo-canceler; PDF estimation; generalized Gaussian model; generalized anti-Hebbian learning; generalized anti-Hebbian rule; generalized blind source separation network; information theory; interference suppression; minimum entropy principle; multidimensional joint probability density function; nonlinearity; performance; simulation; Blind source separation; Entropy; Equations; Gaussian distribution; Jacobian matrices; Laboratories; Multidimensional systems; Neural engineering; Probability density function; Source separation;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on

Conference_Location

Phoenix, AZ

ISSN

1520-6149

Print_ISBN

0-7803-5041-3

Type

conf

DOI

10.1109/ICASSP.1999.759929

Filename

759929