Compression and Enhancement of Speech with Low SNR based on Deep Learning

Aiming at the problem that the compressed sensing algorithm has poor reconstruction performance on low SNR speech, this paper introduces the OPINE-Net
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network, which has shown good performance in image compression. An improved method is proposed on the basis of OPINE-Net
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, which called OPINE-SNet
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, to better adapt to the speech environment. First, the sampling network adaptively learns the sampling matrix from the speech sequence to obtain the measurement value; then, the reconstruction is completed through the initialization network and the reconstruction network in turn; finally, the independent sampling and joint reconstruction are performed using the relationship between blocks. In this paper, Perceptual evaluation of speech quality(PESQ) is selected as speech quality evaluation criteria, the results show that this method has obtained better speech quality and intelligibility.