Threshold reduction for improving Sparse Coding Shrinkage performance in speech enhancement

In this paper, we modify the sparse coding shrinkage (SCS) method with an appropriate optimal linear filter (Wiener filter) in order to improve its efficiency as a speech enhancement algorithm. SCS transform is only applicable for sparse data and speech features do not have this property in either time or frequency domains. Therefore we have used linear independent component analysis (LICA) to transfer the corrupted speech frames to the sparse code space in which noise and speech components are separated by means of a shrinkage function. Before employing SCS, Wiener filtering was applied on the ICA components to reduce noise energy and consequently the SCS shrinkage threshold. Experimental results have been obtained using connected digit database TIDIGIT contaminated with NATO RSG-10 noise data.