Efficient Encoder-Decoder and Dual-Path Conformer for Comprehensive
  Feature Learning in Speech Enhancement
Junyu Wang
Current speech enhancement (SE) research has largely neglected channel
attention and spatial attention, and encoder-decoder architecture-based
networks have not adequately considered how to provide efficient inputs to the
intermediate enhancement layer. To address these issues, this paper proposes a
time-frequency (T-F) domain SE network (DPCFCS-Net) that incorporates improved
densely connected blocks, dual-path modules, convolution-augmented transformers
(conformers), channel attention, and spatial attention. Compared with previous
models, our proposed model has a more efficient encoder-decoder and can learn
comprehensive features. Experimental results on the VCTK+DEMAND dataset
demonstrate that our method outperforms existing techniques in SE performance.
Furthermore, the improved densely connected block and two dimensions attention
module developed in this work are highly adaptable and easily integrated into
existing networks.