Generalization of a maximum-likelihood approach to blind source separation

In the two-source two-sensor blind source separation scenario, only an orthogonal transformation remains to be disclosed once the observations have been whitened. In order to estimate this matrix, a maximum-likelihood (ML) approach has been suggested in the literature, which is only valid for sources with the same symmetric distribution and kurtosis values lying in certain positive range. In the present contribution, the expression for this ML estimator is reviewed and generalized to include almost any source distribution.