Geodesic
On-Line Blind Separation of Non-Stationary Signals
Yugoslav journal of operations research, Tome 15 (2005) no. 1, p. 79

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This paper addresses the problem of blind separation of non-stationary signals. We introduce an on-line separating algorithm for estimation of independent source signals using the assumption of non-stationarity of sources. As a separating model, we apply a self-organizing neural network with lateral connections, and define a contrast function based on correlation of the network outputs. A separating algorithm for adaptation of the network weights is derived using the state-space model of the network dynamics, and the extended Kalman filter. Simulation results obtained in blind separation of artificial and real-world signals from their artificial mixtures have shown that separating algorithm based on the extended Kalman filter outperforms stochastic gradient based algorithm both in convergence speed and estimation accuracy.
Keywords: Blind source separation, decorrelaton, neural networks, extended Kalman filter. 
Slavica Todorović-Zarkula; Branimir Todorović; Miomir Stanković. On-Line Blind Separation of Non-Stationary Signals. Yugoslav journal of operations research, Tome 15 (2005) no. 1, p. 79 . http://geodesic.mathdoc.fr/item/YJOR_2005_15_1_a6/
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     title = {On-Line {Blind} {Separation} of {Non-Stationary} {Signals}},
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