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31 December 2019 Multiple modules speech enhancement in mixed noise and low SNR environments
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Abstract
Achieving stationary speech enhancement in low signal-to-noise ratio (SNR) environments is a challenging problem. Because noise energy is dominant in noisy speech at low SNR level, the existence of numerous obvious random noises may lead neural network to forget some useful information obtained by early training. Moreover, it is difficult for a single neural network to obtain effective speech features and noise features. Therefore, this paper designs to utilize multiple neural networks in two stages to discriminately learn a certain type of noise features and reduce the introduction of interference. Experiment results demonstrate that proposed method leads to consistently better source-to-distortion ratio (SDR) and perceptual evaluation of speech quality (PESQ) than baseline models in low SNR condition. And the results indicate that the method can suppress the forgetting of early information of neural network.
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Tian Lan, Wenzheng Ye, Guoqiang Hui, Sen Li, and Qiao Liu "Multiple modules speech enhancement in mixed noise and low SNR environments", Proc. SPIE 11384, Eleventh International Conference on Signal Processing Systems, 1138406 (31 December 2019); https://doi.org/10.1117/12.2559657
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