The work proposed a denoising speech method using deep learning. The predictor and target network signals were the amplitude spectra of the wavelet-decomposition vectors of the noisy audio signal and clean audio signal, respectively. The output of the network was the amplitude spectrum of the denoised signal. Besides, the regression network used the input of the predictor to minimize the mean square error between its output and input targets. The denoised wavelet-decomposition vector was transformed back to the time domain by the output amplitude spectrum and the phase of the wavelet-decomposition vector. Then, the denoised speech was obtained by the inverse wavelet transform. This method overcame the problem that the frequency and time resolution of the short-time Fourier transform could not be adjusted. The noise reduction effect in each frequency band was improved due to the gradual reduction of the noise energy in the wavelet-decomposition process. The experimental results showed that the method has a good denoising effect in the whole frequency band.

中文翻译：

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基于深度学习和小波分解的语音去噪

该工作提出了一种使用深度学习的去噪语音方法。预测器和目标网络信号分别是噪声音频信号和干净音频信号的小波分解向量的幅度谱。网络的输出是去噪信号的幅度谱。此外，回归网络使用预测器的输入来最小化其输出和输入目标之间的均方误差。通过输出幅度谱和小波分解矢量的相位将去噪后的小波分解矢量转换回时域。然后，通过逆小波变换得到去噪后的语音。该方法克服了短时傅立叶变换的频率和时间分辨率无法调整的问题。由于小波分解过程中噪声能量的逐渐降低，每个频段的降噪效果都得到了提高。实验结果表明，该方法在全频段都具有良好的去噪效果。