BLIND SIGNAL PROCESSING SYSTEM EMPLOYING INFORMATION MAXIMIZATION TO RECOVER UNKNOWN SIGNALS THROUGH UNSUPERVISED MINIMIZATION OF OUTPUT REDUNDANCY

A neural network system and unsupervised learning process for separating unknown source signals from their received mixtures by solving the Independent Components Analysis (ICA) problem. The unsupervised learning procedure solves the general blind signal processing problem by maximizing joint output entropy through gradient ascent to minimize mutual information in the outputs. The neural network system can separate a multiplicity of unknown source signals from measured mixture signals where the mixture characteristics and the original source signals are both unknown. The system can be easily adapted to solve the related blind deconvolution problem that extracts an unknown source signal from the output of an unknown reverberating channel.