MAMGAN: Multiscale attention metric GAN for monaural speech enhancement in the time domain

•This paper proposes a multiscale attention metric GAN for monaural SE in the time domain, which can effectively reduce noise and improve the quality and intelligibility of speech.•This paper proposes a multiscale attention module to act as the bottleneck layer to improve the feature extraction ability of long sequence features.•This paper tests the proposed model on two benchmark datasets. The results show that the proposed method outperforms the current state-of-the-art (SOTA) time-domain SE methods.