The Application of Deep Neural Network in Speech Enhancement Processing

To solve the problem that Non-stationary noise is difficult to remove during speech enhancement process when using Fourier transform, this essay will put forward a speech enhancement algorithm based on the combination of Ensemble Empirical Mode Decomposition (EEMD) and Deep Neural Network (DNN). Firstly, preprocessing the original signal by EEMD, and decomposing a series of time-frequency information of the IMF component to meet the time-variation requirement better; Secondly, adjusting the weight of the IMF component by DNN and then synthesize it to enhanced the speech; Finally, comparing the differences of speech enhancement performance between using EEMD alone, using Fourier transform and EEMD as a preprocessing. The results show that the enhanced algorithm using EEMD as a preprocessing improves the scores of PESQ and STOI by 0.745 and 0.169 respectively, effectively improving the speech quality and intelligibility.