A blind-ML scheme for blind source separation

We present a new approach to the blind source separation problem (BSS, also known as independent component analysis (ICA)), which we term "blind-ML". This approach proposes a framework for estimation of the mixing, which combines a possibly non-parametric distribution estimator with the maximum likelihood estimation of the separating matrix, thereby obtaining both robustness to the sources' densities, and asymptotic efficiency. We provide guidelines for a proof, and verify using simulations, that this approach yields asymptotically efficient (optimal) mean-square-error performance without knowledge of the source densities, and with mild assumptions on the types of sources.