利用深度卷积神经网络提高未知噪声下的语音增强性能

为了进一步提高基于深度学习的语音增强方法在未知噪声下的性能，本文从神经网络的结构出发展开研究.基于在时间与频率两个维度上，语音和噪声信号的局部特征都具有强相关性的特点，采用深度卷积神经网络（Deep convolutional neural network，DCNN）建模来表示含噪语音和纯净语音之间的复杂非线性关系.通过设计有效的训练特征和训练目标，并建立合理的网络结构，提出了基于深度卷积神经网络的语音增强方法.实验结果表明，在未知噪声条件下，本文方法相比基于深度神经网络（Deep neural network，DNN）的方法在语音质量和可懂度两种指标上都有明显提高.

Improving Speech Enhancement in Unseen Noise Using Deep Convolutional Neural Network

In order to further improve the performance of speech enhancement method based on deep learning in unseen noise, this paper focuses on the architecture of neural network. Based on the strong correlation between local characteristics of speech and noise signals in time and frequency domains, a deep convolutional neural network (DCNN) model is used to represent the complex nonlinear relationship between noisy speech and clean speech. By designing effective training features and training target, and establishing reasonable network architecture, a speech enhancement method based on DCNN is proposed. Experimental results show that under the condition of unseen noise, the proposed method significantly outperforms the methods based on deep neural network (DNN) in terms of both speech quality and intelligibility.