Improving Speech Enhancement in Unseen Noise Using Deep Convolutional Neural Network
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摘要: 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.Abstract: 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.1) 本文责任编委 党建武
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图 5 $-5$ dB的Factory2噪声下的增强语音语谱图示例
Fig. 5 An example of spectrogram of enhanced speech under Factory2 noise at $-5$ dB SNR
图 6 卷积层数量对网络性能的影响
Fig. 6 The influence of the number of convolutional layers on the network performance
图 8 $-5$ dB的HF channel噪声下的增强语音语谱图示例
Fig. 8 An example of spectrogram of enhanced speech under HF channel noise at $-5$ dB SNR
图 9 Batch normalization层对网络性能的影响
Fig. 9 The influence of the batch normalization layers on the network performance
图 10 两种特征训练得到的DNN和DCNN的性能比较
Fig. 10 The performance comparisons for DNN and DCNN trained using two kinds of feature
图 11 两种特征训练得到的DNN和DCNN的性能比较
Fig. 11 The performance comparisons for DNN and DCNN trained using two kinds of feature
表 1 三种方法的平均PESQ得分
Table 1 The average PESQ score for three methods
噪声类型 信噪比
(dB)含噪语音 DNN_11F DNN_15F DCNN Factory2 -5 1.73 2.25 2.27 ${\bf 2.33}$ 0 2.07 2.57 2.58 ${\bf 2.65}$ 5 2.40 2.83 2.82 ${\bf 2.89}$ Buccaneer1 -5 1.36 1.88 1.92 ${\bf 1.93}$ 0 1.63 2.24 2.26 ${\bf 2.27}$ 5 1.95 2.54 2.54 ${\bf 2.56} $ Destroyer engine -5 1.59 2.01 1.99 ${\bf 2.15} $ 0 1.81 2.27 2.26 ${\bf 2.46}$ 5 2.10 2.53 2.55 $ {\bf 2.76}$ HF channel -5 1.36 1.7 1.71 ${\bf 2.03} $ 0 1.58 2.04 2.06 ${\bf 2.37}$ 5 1.85 2.38 2.39 ${\bf 2.65}$ 
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表 2 三种方法的平均STOI得分
Table 2 The average STOI score for three methods
噪声类型 信噪比
(dB)含噪语音 DNN_11F DNN_15F DCNN Factory2 -5 0.65 0.76 0.76 ${\bf 0.78 }$ 0 0.76 0.85 0.84 ${\bf 0.86 } $ 5 0.85 0.89 0.89 ${\bf 0.91 }$ Buccaneer1 -5 0.51 0.66 0.66 ${\bf 0.68 }$ 0 0.63 0.77 0.77 ${\bf 0.78 }$ 5 0.75 0.85 0.85 ${\bf 0.86 }$ Destroyer engine -5 0.57 0.62 0.63 ${\bf 0.70 }$ 0 0.69 0.75 0.75 ${\bf 0.82 }$ 5 0.81 0.85 0.85 ${\bf 0.90 }$ HF channel -5 0.57 0.69 0.69 ${\bf 0.73 }$ 0 0.69 0.78 0.79 ${\bf 0.82 }$ 5 0.80 0.86 0.86 ${\bf 0.88 }$
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表 3 三种方法的平均SegSNR
Table 3 The average SegSNR for three methods
噪声类型 信噪比
(dB)含噪语音
(dB)DNN_11F
(dB)DNN_15F
(dB)DCNN
(dB)Factory2 -5 -6.90 -0.69 -0.59 -0.05 0 -4.50 0.34 0.42 0.95 5 -1.57 1.24 1.29 1.80 Buccaneer1 -5 -7.21 -1.52 -1.40 -0.96 0 -4.90 -0.50 -0.39 0.11 5 -2.03 0.46 0.53 1.03 Destroyer engine -5 -7.15 -2.86 -2.81 -2.16 0 -4.90 -1.37 -1.24 -0.54 5 -1.91 0.04 0.21 0.89 HF channel -5 -7.24 -1.13 -1.21 0.35 0 -4.91 0.05 -0.02 1.34 5 -2.09 1.04 1.02 2.03
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