论文标题
通过基于DNN的阶段差异估计的在线阶段重建
Online Phase Reconstruction via DNN-based Phase Differences Estimation
论文作者
论文摘要
本文提出了使用因果深神经网络（DNNS）的两阶段在线阶段重建框架。相重建是仅从相应幅度恢复短时傅立叶变换（STFT）系数的一项任务。但是，相位对波形偏移敏感，即使使用DNN也不容易从大小估计。为了克服这个问题，我们建议使用DNN来估计相邻时频箱之间相位的差异。我们表明，根据STFT相位和幅度的部分衍生物之间的理论关系，卷积神经网络适用于相差估计。估计的相位差异用于重建阶段，通过以逐框方式解决加权最小二乘问题。与现有的基于DNN的相位重建方法相反，所提出的框架是因果关系，不需要任何迭代过程。实验表明，所提出的方法优于现有的在线方法和基于DNN的相位重建方法。
This paper presents a two-stage online phase reconstruction framework using causal deep neural networks (DNNs). Phase reconstruction is a task of recovering phase of the short-time Fourier transform (STFT) coefficients only from the corresponding magnitude. However, phase is sensitive to waveform shifts and not easy to estimate from the magnitude even with a DNN. To overcome this problem, we propose to use DNNs for estimating differences of phase between adjacent time-frequency bins. We show that convolutional neural networks are suitable for phase difference estimation, according to the theoretical relation between partial derivatives of STFT phase and magnitude. The estimated phase differences are used for reconstructing phase by solving a weighted least squares problem in a frame-by-frame manner. In contrast to existing DNN-based phase reconstruction methods, the proposed framework is causal and does not require any iterative procedure. The experiments showed that the proposed method outperforms existing online methods and a DNN-based method for phase reconstruction.
