Competitive algorithm blending for enhanced source separation

This paper proposes to enhance the blind source separation (BSS) solution by running multiple BSS algorithms in parallel and blending the outputs to produce a set of source estimates that is at least as good as any individual method, and potentially better. Although the method is applicable to more general BSS problems, the proposed blending method is described in the case of instantaneous mixtures of stationary, zero-mean, unit-variance, white sources. Experimental results show that the method is able to select a best set of sources with respect to minimum mutual information from an input consisting of source estimates.