A novel adaptive learning rate sequential blind source separation algorithm

摘要

A new member of the family of natural gradient algorithms for on-line blind separation of independent sources is proposed. The method is based upon an adaptive step-size which varies in sympathy with the dynamics of the input signals and properties of the de-mixing matrix, and is robust to the perturbations in the initial value of the learning rate parameter. As a result, the convergence speed is significantly improved, especially in non-stationary mixing environments. Simulations support the expected improvement in convergence speed of the approach.