目前基于深度学习的语音增强方法一般是通过在频域中对语音信号幅度谱进行处理，相位信息受到损失。针对这一问题，提出一种基于时域全卷积网络的语音增强方法。该方法通过设计全卷积神经网络在时域中对语音信号进行处理，保留了信号的原始相位信息，以含噪语音和纯净语音作为网络的输入和输出，建立时域上的非线性关系，实现以端到端的方式进行语音增强。通过仿真实验表明，提出的基于时域全卷积神络语音增强方法在低信噪比的情况下，能够有效地提高语音质量。
At present, speech enhancement methods based on deep learning generally process the amplitude spectrum of speech signal in the frequency domain, and the phase information is lost to some extent. To solve this problem, a speech enhancement method based on time-domain full convolutional network is proposed. The method processes speech signal in time domain by designing full convolutional neural network, and preserves the original phase information of the signal. The noisy speech and clean speech are used as the input and output of the network, and the nonlinear relationship in the time domain is established to realize the end-to-end speech enhancement. The simulation results show that the proposed speech enhancement method based on time-domain full convolution can effectively improve speech quality under the condition of low signal to noise ratio.
2022,44(15): 139-144 收稿日期：2021-08-12
DOI：10.3404/j.issn.1672-7649.2022.15.029
分类号：TN912.35
基金项目：国家自然科学基金资助项目(61771483)
作者简介：李文志(1996-)，男，硕士研究生，研究方向为数字信号处理
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