Blind signal extraction via direct mutual information minimization

This paper presents a new blind signal extraction method based on mutual information. Conventional blind signal extraction methods minimize the mutual information between the extracted signal and the remaining signals indirectly by using a cost function. The proposed method directly minimizes this mutual information through a gradient descent. The derivation of the gradient exploits recent results on the differential of the mutual information and the implementation is based on kernel based density estimation. Some simulation results show the performance of the proposed approach and underline the improvement obtained by using the proposed method as a post-processing for conventional methods.