|On-line blind separation of non-stationary signals
|Yugoslav Journal of Operations Research
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.
|Blind source separation | Decorrelaton | Extended Kalman filter | Neural networks
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checked on Feb 22, 2024
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