Separating Convolutive Mixtures by Mutual Information Minimization
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Adaptive blind separation with an unknown number of sources
Neural Computation
Blind separation of convolutive mixtures by decorrelation
Signal Processing
Blind identification and deconvolution for noisy two-input two-output channels
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
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It is often assumed that blind separation of dynamically mixed sources cannot be done with second-order statistics. It is shown that separation of dynamically mixed sources indeed can be performed using second-order statistics only. A criterion based on second-order statistics for the purpose of separating crosswise mixtures is stated. The criterion is used in order to derive a gradient-based separation algorithm, as well as a Newton-type separation algorithm. The uniqueness of the solution representing the separation is also investigated. This reveals that (1) the channel system is parameter identifiable under weak conditions, and (2) if the sources have the same color, there exists at most two solutions. The local convergence behavior of the proposed algorithm is studied and reveals a sufficient condition for local convergence. Furthermore, the estimates of the channel system are shown to be consistent or to locally minimize the criterion