PCA-Based Compressed Speech Signal Sensing

Abstract: Compressed Sensing theory is a new research focus rising in recent years.Before Compressed Sensing theory is applied to speech signal processing field,a suitable sparse representation for speech signals must be found. Based on principal component analysis theory and a large number of block signals, features of the speech signal are extracted. Moreover,according to Compressed Sensing theory, the method of constructing the dictionary and the characteristics of the speech signal,a kind of new redundant dictionary,the concatenation of some orthogonal bases, for the sparse representation of speech signal is presented in this paper. For more objective description of the advantages of such a sparse representation, two sparsity measures are applied to compare speech signals’sparsity in DCT,GABOR and this redundant dictionary respectively. And male and female speech signals and voiced and unvoiced speech signals are analysed.Simulation results show that whether male or female speech signals and whether voiced and unvoiced speech signals, sparsity of the speech signal in this redundant dictionary is greatly better than the DCT basis and slightly worse than the GABOR basis.However,with its number of atoms far less than GABOR basis and its low computational complexity and storage,this redundant dictionary is more applicable than GABOR basis to speech signal.