Recent work has shown that it is feasible to use generative adversarial
networks (GANs) for speech enhancement, however, these approaches have not been
compared to state-of-the-art (SOTA) non GAN-based approaches. Additionally,
many loss functions have been proposed for GAN-based approaches, but they have
not been adequately compared. In this study, we propose novel convolutional
recurrent GAN (CRGAN) architectures for speech enhancement. Multiple loss
functions are adopted to enable direct comparisons to other GAN-based systems.
The benefits of including recurrent layers are also explored. Our results show
that the proposed CRGAN model outperforms the SOTA GAN-based models using the
same loss functions and it outperforms other non-GAN based systems, indicating
the benefits of using a GAN for speech enhancement. Overall, the CRGAN model
that combines an objective metric loss function with the mean squared error
(MSE) provides the best performance over comparison approaches across many
evaluation metrics.

本研究提出了一种卷积循环 GAN (CRGAN) 架构，并采用多种损失函数与其他基于 GAN 的系统进行了直接比较。结果表明，该 CRGAN 模型在同样的损失函数下优于其他基于 GAN 的模型，并且超越其他非 GAN 的系统，表明了 GAN 在语音增强方面的优势。综合评估指标，将客观度量损失函数与均方误差 (MSE) 结合的 CRGAN 模型表现最佳。