@@ -8,7 +8,7 @@ This repository contains the official PyTorch implementations for the papers:
- Julius Richter, Simon Welker, Jean-Marie Lemercier, Bunlong Lay, Timo Gerkmann. [*"Speech Enhancement and Dereverberation with Diffusion-Based Generative Models"*](https://ieeexplore.ieee.org/abstract/document/10149431), IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 2351-2364, 2023. [[bibtex]](#citations--references)
- Julius Richter, Yi-Chiao Wu, Steven Krenn, Simon Welker, Bunlong Lay, Shinji Watanabe, Alexander Richard, Timo Gerkmann. [*"EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation"*](https://arxiv.org/abs/2406.06185), ISCA Interspecch, Kos, Greece, 2024. [[bibtex]](#citations--references)
Audio examples and further supplementary materials are available on our [SGMSE project page](https://www.inf.uni-hamburg.de/en/inst/ab/sp/publications/sgmse) and [EARS project page](https://sp-uhh.github.io/ears_dataset/)
Audio examples and supplementary materials are available on our [SGMSE project page](https://www.inf.uni-hamburg.de/en/inst/ab/sp/publications/sgmse) and [EARS project page](https://sp-uhh.github.io/ears_dataset/)
## Follow-up work
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@@ -56,6 +56,7 @@ To see all available training options, run `python train.py --help`. Note that t
- For the 48 kHz model [3], use `--backbone ncsnpp_48k --spec_factor 0.065 --spec_abs_exponent 0.667 --sigma-min 0.1 --sigma-max 1.0 --theta 2.0`
- Our Interspeech paper [1] uses `--backbone dcunet`. You need to pass `--n_fft 512` to make it work.
- Also note that the default parameters for the spectrogram transformation in this repository are slightly different from the ones listed in the first (Interspeech) paper (`--spec_factor 0.15` rather than `--spec_factor 0.333`), but we've found the value in this repository to generally perform better for both models [1] and [2].
