Flexible Score Functions for Blind Separation of Speech Signals Based on Generalized Gamma Probability Density Functions

In this contribution, we propose an entirely novel family of flexible score functions for blind source separation (BSS), based on the generalized Gamma family of densities. An efficient maximum likelihood (ML) technique for estimating the parameters of such score functions in an adaptive BSS setup, is also put forward. Simulations indicate that the proposed density model can approximate speech signals more accurately than conventional distributions, which leads to an increase in separation performance and convergence speed