Blind Separation of Nonstationary Markovian Sources Using an Equivariant Newton–Raphson Algorithm

This letter presents a new maximum likelihood method for blindly separating linear instantaneous source mixtures, where source signals are assumed to be mutually independent, Markovian and possibly nonstationary. The proposed approach first extends previous works, by Hosseini to possibly nonstationary sources using two approaches based on blocking and kernel smoothing, respectively. Moreover, to reduce time consumption, we propose an equivariant modified Newton-Raphson algorithm to solve the estimating equations, and we introduce polynomial estimators for the conditional score functions used in our method. Experimental results, both for artificial and real (speech) signals, prove the better performance of our method as compared to various classical blind separation algorithms.