Compressive sensing based low bit rate speech encoder

Speech coding is a representation of a digitized speech signal using as few bits as possible, while maintaining reasonable level of speech quality. Due to growing need for bandwidth conservation in wireless communication, the research in speech coding has increased. Compressive Sensing (CS) is gaining a great interest because of its ability to recover original signals by taking only few measurements. CS is a new approach that is different from the common data acquisition methods. In this research, a new approach of speech encoding system is developed using compressive sensing. Since CS performs well in sparse signals, different sparsifying transforms is analyzed and compared using Gini coefficient. The quality of the speech coder is evaluated using Perceptual Evaluation of Speech Quality (PESQ), Signal-to-Noise Ratio (SNR) and subjective listening tests. Results show that the speech coders have achieved a PESQ score of 3.16 at 4 kbps which is a good quality as listening tests confirms. The coder is also compared with Code Excited Linear Prediction (CELP) coder.