Install requirements with pip instlal -r requirements.txt.
GPU-acceleration is supported for both the DNNs and the DSP operations
This code is for inference only, training loops are unfortunately not being made available
To download the models, please use this link and put the obtained .pt files in ./models
To perform inference, simply use
python3 derev.py --speech <speech_file_path> --config <config_key>
with one of the following config_key:
wpe_ci: End-to-end optimized multi-channel linear filter targeted for cochlear implant users (few early reflections)wpe_ha: End-to-end optimized multi-channel linear filter targeted for hearing-aid users (more early reflections)wpe+pf_ci: End-to-end optimized multi-channel linear filter + non-linear single-channel post-filter targeted for cochlear implant users (few early reflections)wpe+pf_ha(default, recommended): End-to-end optimized multi-channel linear filter + non-linear single-channel post-filter targeted for hearing-aid users (more early reflections)
Please consider citing our work if you found this useful:
@article{lemercier2022a,
author={Lemercier, Jean-Marie and Thiemann, Joachim and Koning, Raphael and Gerkmann, Timo},
title={A neural network‐supported two‐stage algorithm for lightweight dereverberation on hearing devices},
year={2023},
journal={EURASIP Journal on Audio, Speech, and Music Processing},
volume={18},
pages={1-12},
doi={https://doi.org/10.1186/s13636-023-00285-8},
}