Evaluating performance of Compressed Sensing for speech signals

Reconstruction of a signal based on Compressed Sensing (CS) framework relies on the knowledge of the sparse basis & measurement matrix used for sensing. While most of the studies so far focus on the application of CS in fields of images, radar, astronomy etc.; we present our work on application of CS in field of speech/Audio processing. This work shows a comparative analysis of different sparse basis & measurement matrices which can be used in speech/audio processing. Our work gives a detail analysis of the performance bounds, compression ratios, reconstruction errors etc. which should be taken care of while designing CS based speech applications.