Temporal decorrelation using teacher forcing anti-Hebbian learning and its application in adaptive blind source separation

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.