Blind source separation from hybrid mixture based on nonlinear InfoMax approach

In this paper, we show that the nonlinear InfoMax algorithm in blind source separation is also based on the contrast function of Kullback-Leibler divergence under certain conditions. Its high separating performance for speech sources is closely related to the fact that the selected nonlinear functions approximate the probability density functions (PDFs) of source signals. With this understanding, we propose a new nonlinear InfoMax algorithm in which the nonlinear functions are iteratively updated simultaneously with the estimation of the unmixing matrix. Simulation results show that the algorithm can extract independent sources from the hybrid mixture of any super-Gaussian and sub-Gaussian signals