中圖分類號(hào)： TN912.3 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.200327 中文引用格式： 夏鼎，徐文濤. 基于生成對(duì)抗網(wǎng)絡(luò)合成噪聲的語音增強(qiáng)方法研究[J].電子技術(shù)應(yīng)用，2020，46(11)：56-59，64. 英文引用格式： Xia Ding，Xu Wentao. Research on speech enhancement method based on generating noise using GAN[J]. Application of Electronic Technique，2020，46(11)：56-59，64.
Research on speech enhancement method based on generating noise using GAN
Xia Ding，Xu Wentao
School of Science，Nanjing University of Aeronautics and Astronautics，Nanjing 211106，China
Abstract： In the field of speech enhancement, deep neural network can improve the enhancement ability of the model by training and modeling a large number of data with different noises in the supervised learning way. However, the acquisition cost of different types of noise is large and the noise types are difficult to be comprehensive, which affects the generalization ability of the model. Aiming at this problem, this paper proposes a noise data augmentation method based on generative adversarial network(GAN), which learns from the real noise data and synthesizes virtual noises according to the data features, so as to expand the number and type of the noise data in the training set. Experimental results show that the method of noise synthesis adopted in this article can effectively expand the source of noise in the training set, enhance the generalization ability of the model, and effectively improve the signal-to-noise ratio and intelligibility of speech signal after denoising.
Key words : speech enhancement；generative adversarial network；data augmentation
