基于生成对抗网络合成噪声的语音增强方法研究

Research on speech enhancement method based on generating noise using GAN

在语音增强领域,深度神经网络通过对大量含有不同噪声的语音以监督学习方式进行训练建模,从而提升网络的语音增强能力。然而不同类型噪声的获取成本较大,噪声类型难以全面采集,影响了模型的泛化能力。针对这个问题,提出一种基于生成对抗网络(Generative Adversarial Networks,GAN)的噪声数据样本增强方法,该方法对真实噪声数据进行学习,根据数据特征合成虚拟噪声,以此扩充训练集中噪声数据的数量和类型。通过实验验证,所采用的噪声合成方法能够有效扩展训练集中噪声来源,增强模型的泛化能力,有效提高语音信号去噪处理后的信噪比和可理解性。

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.