In order to improve the performance of speech enhancement networks by making full use of noisy speech features

based on the correlation of noisy speech in time and frequency

by combining the local feature extraction ability of convolutional neural networks and the long-term dependence modeling ability of gated recurrent unit

a convolutional gated recurrent network suitable for speech enhancement is designed in this paper. This network uses a convolutional network structure instead of a fully connected network structure to improve the feature calculation process in the gated recurrent unit

thereby can better retain the time-frequency structure in the noisy speech features. The experimental results show that compared with other speech enhancement networks

the proposed network has obvious advantages in speech component retention and noise component suppression

and the enhanced speech has better speech quality and intelligibility.