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This contribution details the development of a mask-based post- processor to improve the interference suppression in speech signals separated using linear deconvolution algorithms like independent component analysis (ICA). The design of the proposed post-filter is in two stages: in the first stage, use is made of the disjointness of the separated signals in the time-frequency domain to obtain binary masks to suppress cross-talk that generally remains after separation. In the next stage, a novel smoothing of the masks is proposed that preserves the speech structure of the target source while eliminating the random peaks in the time-frequency plane that lead to fluctuating background noise. The result is an enhanced signal with reduced cross-talk and no musical noise.