Blind Separation of Filtered Sources Using State-Space Approach

Part of Advances in Neural Information Processing Systems 11 (NIPS 1998)

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Liqing Zhang, Andrzej Cichocki


In this paper we present a novel approach to multichannel blind separation/generalized deconvolution, assuming that both mixing and demixing models are described by stable linear state-space sys(cid:173) tems. We decompose the blind separation problem into two pro(cid:173) cess: separation and state estimation. Based on the minimization of Kullback-Leibler Divergence, we develop a novel learning algo(cid:173) rithm to train the matrices in the output equation. To estimate the state of the demixing model, we introduce a new concept, called hidden innovation, to numerically implement the Kalman filter. Computer simulations are given to show the validity and high ef(cid:173) fectiveness of the state-space approach.