Speech Enhancement Algorithm Combining Cochlear Features and Deep Neural Network with Skip Connections

To solve the problem of the poor enhancement effect of traditional deep learning-based speech enhancement algorithms in low signal-to-noise ratio (SNR) scenarios, this paper proposes a method combining front-end processing Multi-Resolution Cochleagram(FP-MRCG) and skip connections deep neural network (Skip-DNN). This method uses FP-MRCG speech features to train Skip-DNN, and estimates the ideal ratio mask, filters out the background noise of the noisy speech to obtain the enhanced speech features, and obtains enhanced speech by phase reconstruction. The result shows that when the SNR is 0dB, using FP-MRCG as Skip-DNN’s input, the average perceptual evaluation of speech quality (PESQ) of enhanced speech is 2.5283, and the average short-term objective intelligibility (STOI) is 0.8825, which is 3%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} and 1.7%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} higher than MRCG, respectively. Besides, when using FP-MRCG as the input of DNN, Skip-DNN and convolutional neural network (CNN), Skip-DNN has a higher evaluation score in a low SNR environment, and CNN has a higher evaluation score in a high SNR environment. However, the training time for the CNN is twice as long as that for the Skip-DNN. Hence, it can be concluded that Skip-DNN performs better in speech enhancement than the other two networks.