Deep Neural Network speech Enhancement method based on singular Spectrum Analysis

In order to solve the problems of limited denoising ability and low speech quality improvement of existing deep neural network speech enhancement methods for noisy speech, a deep neural network speech enhancement method based on singular spectrum analysis is proposed. By introducing the singular spectrum analysis algorithm to preprocess the noisy speech, the speech signal and noise are preliminarily separated. Then the speech signal and noise are used to train the depth neural network model to obtain a network model with better performance, so that the new method of deep neural network speech enhancement based on singular spectrum analysis has better performance. Finally, both the logarithmic power spectrum estimated by the neural network and the logarithmic power spectrum of noisy speech are used to reconstruct clean speech. The method proposed in this paper can adapt to the situation of different signal-to-noise ratio, effectively remove the background noise and reduce the distortion of speech signal. In this paper, simulation experiments are carried out to verify the effectiveness and robustness of the method.