TEMPORAL DECORRELATION USING TEACHER FORCING
      ANTI-HEBBIAN LEARNING AND ITS APPLICATION IN
            ADAPTIVE BLIND SOURCE SEPARATION

      Jose C. Principe, Chuan Wang*, and Hsiao-Chun Wu

         Computational NeuroEngineering Laboratory
     Department of Electrical and Computer Engineering
       University of Florida, Gainesville, FL 32611
               principe@synapse.ee.ufl.edu

     * now with Lucent Technologies Inc., New Jersey.


                         Abstract 

This paper proposes a network architecture to compute on-line   
the temporal crosscorrelation function between two signals, either   
stationary or locally stationary. We show that the weights of a multi-FIR   
(Finite Impulse Response) filter trained with a teacher forcing   
anti-Hebbian rule encode the crosscorrelation function between the input   
and the desired response. We extend this network to the Gamma filter   
which is an IIR (Infinite Impulse Response) filter and also to nonlinear   
filters. This temporal correlation idea is applied to the blind source   
separation problem. From these networks we build a recurrent system   
trained on-line with anti-Hebbian learning which performs temporal   
decorrelation on the mixed signals. The system performance is tested in   
speech signals mixed in time with good results. A comparison of the   
performance among the different topologies is also presented in the   
paper. Moreover, we pointed out some very interesting observations we   
obtained and open problems for further research.

Acknowledgments: This work was partially supported by ARPA/ONR grant   
N00014-94-1-0858.