Speech Enhancement Model and Algorithm Based on Sparse Signal Reconstruction in Compressive Sensing

The objective of speech enhancement is to eliminate noise interference in noisy speech signal and to improve both speech quality and speech intelligibility. Different from traditional speech enhancement algorithms, this paper utilizes the difference of sparsity between speech and noise signal, presents the speech enhancement model based on sparse signal reconstruction in compressive sensing and draws its mathematical expression. According to this speech enhancement model, this paper also takes into account the sparsity and non-stationarity of speech signal, and proposes an orthogonal matching pursuit speech enhancement algorithm weighted with speech presence probability. Experimental results show that the proposed speech enhancement model and algorithm is feasible, effective and superior. The proposed algorithm not only can eliminate noise interference but also can reserve most of speech signal. Therefore, the objective of speech enhancement is attained. Furthermore, compared with spectral subtraction algorithm and minimum mean square error algorithm, the proposed algorithm is less efficiently computable, however, its performance is better.