Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Natural gradient works efficiently in learning
Neural Computation
Blind equalisation with recursive filter structures
Signal Processing - Special section on signal processing technologies for short burst wireless communications
Modeling MPEG Coded Video Traffic by Markov-Modulated Self-Similar Processes
Journal of VLSI Signal Processing Systems
IEEE Transactions on Signal Processing
Fast maximum likelihood for blind identification of multiple FIRchannels
IEEE Transactions on Signal Processing
Multichannel blind deconvolution of nonminimum-phase systems using filter decomposition
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Mutual information approach to blind separation of stationary sources
IEEE Transactions on Information Theory
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We introduce a novel cascade demixing structure for multichannel blind deconvolution in nonminimum phase systems. To simplify the learning process, we decompose the demixing model into a causal finite impulse response (FIR) filter and an anticausal scalar FIR filter. A permutable cascade structure is constructed by two subfilters. After discussing geometrical structure of FIR filter manifold, we develop the natural gradient algorithms for both FIR subfilters. Furthermore, we derive the stability conditions of algorithms using the permutable characteristic of the cascade structure. Finally, computer simulations are provided to show good learning performance of the proposed method.