Blind inversion of Wiener system for single source using nonlinear blind source separation

In this paper, a nonlinear blind source separation system with post-nonlinear mixing; model, and an unsupervised learning algorithm for the parameters of this separating system are presented for blind inversion of Wiener system for single source. The proposed method firstly changes the deconvolution part of Wiener system into a special case of linear blind source separation (BSS). Then the nonlinear BSS system is applied to derive the source signal. The proposed nonlinear BSS method can dynamically estimate the nonlinearity of mixing model and adapt to the cumulative probability function (CPF) of sources. Finally, experimental results demonstrate that our proposed method is effective and efficient for the problems addressed.