  In this paper, we propose a transformer-based architecture, called two-stage
transformer neural network (TSTNN) for end-to-end speech denoising in the time
domain. The proposed model is composed of an encoder, a two-stage transformer
module (TSTM), a masking module and a decoder. The encoder maps input noisy
speech into feature representation. The TSTM exploits four stacked two-stage
transformer blocks to efficiently extract local and global information from the
encoder output stage by stage. The masking module creates a mask which will be
multiplied with the encoder output. Finally, the decoder uses the masked
encoder feature to reconstruct the enhanced speech. Experimental results on the
benchmark dataset show that the TSTNN outperforms most state-of-the-art models
in time or frequency domain while having significantly lower model complexity.
