Blind Speech Separation Employing Laplacian Normal Mixture Distribution Model
Hua Cai, Junxi Sun, Shifeng Ou · 2007
Careful choice of nonlinear function is necessary to obtain good performance from algorithms for blind source separation. In this paper, we propose a fast approach to perform blind speech separation based on natural gradient. The main ingredient is the use of a novel nonlinear function, which is accordant to the true PDF of speech signals. By appropriately choosing the shape parameter, we approximate a Laplacian normal mixture distribution to the source's PDF in time domain, then a new form of nonlinear function more suitable for speech separation is derived using the given distribution model. Simulation results indicate the good convergence and steady-state performance of our proposed method.