Adaptive approach to blind source separation with cancellation of additive and convolutional noise

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