Paper Info
Title
DPT-FSNet: Dual-Path Transformer Based Full-Band and Sub-Band Fusion Network for Speech Enhancement
Abstract
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).
Year
DOI
Venue
2022
10.1109/ICASSP43922.2022.9746171
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Feng Dang101.01
Hangting Chen201.35
Pengyuan Zhang35019.46