CAUNet: Context-Aware U-Net for Speech Enhancement in Time Domain
Kai Wang, Bengbeng He, Wei‐Ping Zhu · 2021
In this paper, we propose a transformer neural network based U-Net architecture, called context-aware U-Net (CAUNet), for end-to-end speech denoising in time domain. The proposed model adopts the dilated-dense block in both encoder and decoder layers of the U-Net to strengthen feature propagation and enlarge the receptive field of features. It also uses stacked two-stage transformer blocks to efficiently extract local and global contextual information from the encoder output, based on which the enhanced speech is reconstructed at the decoder. Experimental results show that our model outperforms most state-of-the-art methods in time and frequency domains, while it maintains a relatively low model complexity.