Abstract
The paper discusses state space blind source recovery (BSR) for minimum phase and non-minimum phase mixtures of Gaussian and non-Gaussian distributions. The state space natural gradient approach results in compact iterative update laws for BSR. Two separate state space algorithms for minimum phase and non-minimum phase mixing environments are presented. The advantages and disadvantages of both algorithms in the context of multiple source distribution mixtures are examined. The presented BSR algorithms require use of nonlinearities, which depend on the distribution of the unknown sources. We propose use of an adaptive nonlinearity based on the batch kurtosis of the output. This renders the adaptive estimation of the demixing network completely blind.