Blind Signal Separation by Matching Pursuit Based Grouping

This paper describes a novel matching pursuit based grouping approach for separating a speech signal from a mixture with non-Gaussian interference. At first, the mixture signal is decomposed into atoms by matching pursuit with a Gabor dictionary. Then a psychoacoustic based grouping algorithm is developed to cluster the atoms into groups to identify the atoms of a speech signal. These atoms are then used to reconstruct the desired speech signal. Simulations were performed on speech corrupted by factory noise and music. Preliminary results show that the proposed approach can remove almost all non-speech signal while the recovered speech signal possesses acceptable intelligibility.