This repo provides a reference implementation of IDANet as described in the paper:
IDANet: An Information Distillation and Aggregation Network for Speech Enhancement
Accepted by SPL 2021
105 types of noise are concatenated for training and validation, while 110 types of noise for testing (add 5 unseen noises). The ratio of the invisible part to the visible part of our test noise is about 4:1 (5 unseen noises vs. 105 seen noises, 20 minutes vs. 5 minutes)
The model's code we use for DARCN is from their official depository (https://github.com/Andong-Li-speech/DARCN). For CRN, we use an unofficial code implemented from https://github.com/haoxiangsnr/A-Convolutional-Recurrent-Neural-Network-for-Real-Time-Speech-Enhancement. Since GRN did not publish their code, we reproduce it according to the original paper.
The experimental platform is Ubuntu LTS 18.04 with i7-9700 and RTX 2060.
If you find the code useful for your research, please consider citing
@article{tai2021idanet,
title={IDANet: An Information Distillation and Aggregation Network for Speech Enhancement},
author={Tai, Wenxin and Lan, Tian and Wang, Qianhui and Liu, Qiao},
journal={IEEE Signal Processing Letters},
volume={28},
pages={1998--2002},
year={2021},
publisher={IEEE}
}For any questions please open an issue or drop an email to: wxtai@std.uestc.edu.cn