Blind source separation and deconvolution by dynamic component analysis

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.