Blind separation and blind deconvolution: an information-theoretic approach
Blind separation and blind deconvolution are related problems in unsupervised learning. In this contribution, static non-linearities are used in combination with an information-theoretic objective function, making the approach more rigorous than previous ones. We derive a new algorithm and with it perform nearly perfect separation of up to 10 digitally mixed human speakers, better performance than any previous algorithms for blind separation. When used for deconvolution, the technique automatically cancels echoes and reverberations and reverses the effects of low-pass filtering.