A Convolutional Gated Recurrent Network for Speech Enhancement

被引：0

作者：

Yuan W.-H. ^{[1
]}

Hu S.-D. ^{[1
]}

Shi Y.-L. ^{[1
]}

Li Z. ^{[1
]}

Liang C.-Y. ^{[1
]}

机构：

[1] College of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, Shandong

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2020年 / 48卷 / 07期

关键词：

Convolutional neural network; Deep neural network; Gated recurrent unit; Speech enhancement;

D O I：

10.3969/j.issn.0372-2112.2020.07.005

中图分类号：

学科分类号：

摘要：

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. © 2020, Chinese Institute of Electronics. All right reserved.

引用

页码：1276 / 1283

页数：7

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