  Generative adversarial network (GAN) still exists some problems in dealing
with speech enhancement (SE) task. Some GAN-based systems adopt the same
structure from Pixel-to-Pixel directly without special optimization. The
importance of the generator network has not been fully explored. Other related
researches change the generator network but operate in the time-frequency
domain, which ignores the phase mismatch problem. In order to solve these
problems, a deep complex convolution recurrent GAN (DCCRGAN) structure is
proposed in this paper. The complex module builds the correlation between
magnitude and phase of the waveform and has been proved to be effective. The
proposed structure is trained in an end-to-end way. Different LSTM layers are
used in the generator network to sufficiently explore the speech enhancement
performance of DCCRGAN. The experimental results confirm that the proposed
DCCRGAN outperforms the state-of-the-art GAN-based SE systems.
