Multi Dimensional ICA to Separate Correlated Sources


We present a new method for the blind separation of sources, which do not fulfill the independence assumption. In contrast to standard methods we consider groups of neighboring samples ("patches") within the observed mixtures. First we extract independent features from the observed patches. It turns out that the average dependencies between these features… (More)


3 Figures and Tables

Slides referencing similar topics