<title>Abstract</title> Transformer can be effectively applied to speech enhancement tasks based on Generative Adversarial Network (GAN). However, it still remains challenging to extract temporal dependencies within the signal sequence features as well as to improve training stability. To address these issues, a new light-weight network is proposed for speech enhancement in the time-frequency domain named CRG-MGAN. It is a type of MetricGAN based on convolution and recurrent-augmented spatially gated attention. In…