  Recently, dual-path networks have achieved promising performance due to their
ability to model local and global features of the input sequence. However,
previous studies are based on simple time-domain features and do not fully
investigate the impact of the input features of the dual-path network on the
enhancement performance. In this paper, we propose a dual-path
transformer-based full-band and sub-band fusion network (DPT-FSNet) for speech
enhancement in the frequency domain. The intra and inter parts of the dual-path
transformer network in our model can be seen as sub-band and full-band modeling
respectively, which have stronger interpretability as well as more information
compared to the features utilized by the time-domain transformer. We conducted
experiments on the Voice Bank + DEMAND dataset to evaluate the proposed method.
Experimental results show that the proposed method outperforms the current
state-of-the-arts in terms of PESQ, STOI, CSIG, COVL. (The PESQ, STOI, CSIG,
and COVL scores on the Voice Bank + DEMAND dataset were 3.30, 0.95, 4.51, and
3.94, respectively).
