Blind Extraction of Noisy Events using Nonlinear Predictor

Existing blind source extraction (BSE) methods are limited to noise-free mixtures, which is not realistic. We therefore address this issue and propose an algorithm based on the normalised kurtosis and a nonlinear predictor within the BSE structure, which makes this class of algorithms suitable for noisy environments, a typical situation in practice. Based on a rigorous analysis of the existing BSE methods we also propose a new optimisation paradigm which aims at minimising the normalised mean square prediction error (MSPE). This makes redundant the need for preprocessing or orthogonality transform. Simulation results are provided which confirm the validity of the theoretical results and demonstrate the performance of the derived algorithms in noisy mixing environments.