Mirror model gives separation of convolutive mixing of PNL mixtures

The proof is given that the so called ʹmirror modelʹ as demixing model is able to recover original sources after non-trivial mixing. The issue explored is the capability to separate sources, in a blind way, after the convolutive mixing of post nonlinear (PNL) mixtures. The strictness of that kind of mixture produces non-trivial problems in separating signals without any adequate assumption on recovering architecture.