A Method of Underdetermined Blind Source Separation with an Unknown Number of Sources

Abstract

Aiming to source number estimation, the recovery of mixing matrix and source signal under underdetermined case, we propose a method of underdetermined blind source separation with an unknown number of sources. Firstly, we introduced an algorithm based on S transform and fuzzy c-means clustering technique to estimate number of sources and mixing mixtures. Then sources are represented as null space form and the source signals are recovered by using an algorithm based on Maximum Likelihood. The simulation results show that the proposed method can separate sources of any distribution, and it has superior evaluation performance to the conventional methods.