Sparse Representation and Recovery of a Class of Signals Using Information Theoretic Measures

In this paper, we discuss a novel scheme for arriving at a sparse representation and recovery of a class of signals using information theoretic measures. Constituent components containing distinct features of any signal, belonging to a specific class, are separated and represented sparsely in an appropriate fixed basis. The morphological correlation between each of the constituent components and a subset of basis leads to sparse representation of the signal in that basis. The basis is selected using entropy minimization based method which is known to result in coefficient concentration. Simulation studies on speech signals show that in the presence of input noise, the proposed method outperforms conventional methods.