  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.

Sound;Artificial Intelligence;Audio and Speech Processing