Adaptive Bayesian compressed sensing based on speech frame signal

Compressed Sensing (CS) is an emerging theory which can sample the sparse signal or compressible signal via sub-Nyquist sampling rate and reconstruct the original signal with small amount of measurements. Since speech signal is sparse in Discrete Cosine Transform (DCT) domain, a kind of adaptive Bayesian Compressed Sensing (BCS) based on speech signal is proposed in this paper. In the one hand, our proposed method exploits the difference of energy within different speech frame to allot measurements adaptively for each speech frame aim to promote the quality of recovery speech signal. In the other hand, the position information of sparse coefficient in each speech frame is also utilized by our proposed method to recover its neighboring speech frame for reducing the recovery time of speech signal. The experimental results prove that our proposed method is surely effective and practical.