Underdetermined Blind Source Separation based on Fuzzy C-Means and Semi-Nonnegative Matrix Factorization

Conventional blind source separation is based on over-determined with more
sensors than sources but the underdetermined is a challenging case and more convenient
to actual situation. Non-negative Matrix Factorization (NMF) has been widely applied to
Blind Source Separation (BSS) problems. However, the separation results are sensitive
to the initialization of parameters of NMF. Avoiding the subjectivity of choosing
parameters, we used the Fuzzy C-Means (FCM) clustering technique to estimate the
mixing matrix and to reduce the requirement for sparsity. Also, decreasing the constraints
is regarded in this paper by using Semi-NMF. In this paper we propose a new two-step
algorithm in order to solve the underdetermined blind source separation. We show how
to combine the FCM clustering technique with the gradient-based NMF with the multilayer
technique. The simulation results show that our proposed algorithm can separate
the source signals with high signal-to-noise ratio and quite low cost time compared with
some algorithms