Implementing Nonlinear Algorithm in Multimicrophone Signal Processing

We address in this paper a method for blind source separation of multi-microphone signals. The multi-microphone is modelled as a nonlinear mapping system, the nonlinear characteristic takes into consideration the sensor effect and natural phenomena. The observations (recorded signals) are modelled as post nonlinear mixtures. The proposed nonlinear algorithm is a generalization of serial gradient algorithm, cross-correlations, and Gram-Charlier series, which is extended in two ways: (1) to deal with nonlinear mapping, and (2) to be able to adapt to the actual statistical distributions of the sources by estimating the kernel density distribution at the output signals. The theory of the proposed learning algorithm is discussed. Simulations show that the algorithm is able to find the underlying sources from the post-nonlinear mixture observations