Convolutive Nonlinear Separation with Unsupervised Neural Network

Abstract

In this paper we propose a separating system for convolutive nonlinear mixtures, since it is rare to find linear instantaneous mixture in practical problems. The observed mixtures are first transformed into frequency domain and then separated on each frequency bin. After solved permutation problem, estimates are obtained. We show results of simulations with acoustic signals both of instantaneous and convolutive mixtures.