Title :
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
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;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on
Conference_Location :
Phoenix, AZ
Print_ISBN :
0-7803-5041-3
DOI :
10.1109/ICASSP.1999.759929
