Adaptive Approach to Blind Source Separation with Cancellation of Additive and Convolutional Noise

In this paper an adaptive approach to cancellation of additive, convolutional noise from many{source mixtures with a simultaneous blind source separation is proposed. Associated neural network learning algorithms are developed on the basis of decorrelation principle and energy minimization of output signals. The reference noise is transformed into a convolutional one by employing an adaptive FIR lter in each channel. Several models of NN learning processes are considered. In the basic approach the noisy signals are separated simultaneously with the additive noise cancellation. The simpli ed model employs separate learning steps for noise cancellation and source separation. Multi{layer neural networks improve the quality of results. Results of comparative tests of proposed methods are provided.