Blind source separation and deconvolution by dynamic component analysis
Hagai T. Attias, Christoph E. Schreiner · 2002
We derive new unsupervised learning rules for blind separation of mixed and convolved sources. These rules are nonlinear in the signals and thus exploit high-order spatiotemporal statistics to achieve separation. The derivation is based on a global optimization formulation of the separation problem, yielding a stable algorithm. Different rules are obtained from frequency- and time-domain optimization. We illustrate the performance of this method by successfully separating convolutive mixtures of speech signals.