Blind separation of convolutive mixtures of speech signals using linear combination model

In this paper, we propose a blind separation algorithm for convolutive mixture of source signals on the basis of the information-theoretical approach. This approach requires distribution models of the sources. It is difficult to select the models without prior knowledge of sources. In order to resolve the difficulty, we introduce a distribution model with parameters. We construct the parametric model by linearly combining two density functions corresponding to sub- and super-Gaussian distributions. Our algorithm adaptively estimates the parameters and designs a separating filter. We applied the algorithm to convolutive mixtures of two speeches in a real environment. The result of our experiments shows that our algorithm can improve separation performance.