Cycle-consistent generative adversarial networks (CycleGAN) have shown their
promising performance for speech enhancement (SE), while one intractable
shortcoming of these CycleGAN-based SE systems is that the noise components
propagate throughout the cycle and cannot be completely eliminated.
Additionally, conventional CycleGAN-based SE systems only estimate the spectral
magnitude, while the phase is unaltered. Motivated by the multi-stage learning
concept, we propose a novel two-stage denoising system that combines a
CycleGAN-based magnitude enhancing network and a subsequent complex spectral
refining network in this paper. Specifically, in the first stage, a
CycleGAN-based model is responsible for only estimating magnitude, which is
subsequently coupled with the original noisy phase to obtain a coarsely
enhanced complex spectrum. After that, the second stage is applied to further
suppress the residual noise components and estimate the clean phase by a
complex spectral mapping network, which is a pure complex-valued network
composed of complex 2D convolution/deconvolution and complex temporal-frequency
attention blocks. Experimental results on two public datasets demonstrate that
the proposed approach consistently surpasses previous one-stage CycleGANs and
other state-of-the-art SE systems in terms of various evaluation metrics,
especially in background noise suppression.

本文提出了一种基于两个阶段的去噪系统，该系统通过将 CycleGAN 模型的估计强度与原始嘈杂的相位相结合来获得粗略增强的复杂光谱，并利用复杂光谱映射网络进一步抑制噪声成分和估计干净相位。实验结果表明，该方法在各种评估指标方面始终优于之前的单阶段 CycleGAN 和其他最先进的 SE 系统，尤其是在背景噪声抑制方面