Asteroid: the PyTorch-based audio source separation toolkit for researchers
Paper
•
2005.04132
•
Published
mpariente/DPRNNTasNet(ks=16)_WHAM!_sepclean
♻️ Imported from https://zenodo.org/record/3903795#.X8pMBRNKjUI
This model was trained by Manuel Pariente using the wham/DPRNN recipe in Asteroid. It was trained on the sep_clean task of the WHAM! dataset.
# coming soon
si_sdr: 18.227683982688003si_sdr_imp: 18.22883576588251sdr: 18.617789605060587sdr_imp: 18.466745426438173sir: 29.22773720052717sir_imp: 29.07669302190474sar: 19.116352171914485sar_imp: -130.06009796503054stoi: 0.9722025377865715stoi_imp: 0.23415680987800583@inproceedings{Pariente2020Asteroid,
title={Asteroid: the {PyTorch}-based audio source separation toolkit for researchers},
author={Manuel Pariente and Samuele Cornell and Joris Cosentino and Sunit Sivasankaran and
Efthymios Tzinis and Jens Heitkaemper and Michel Olvera and Fabian-Robert Stöter and
Mathieu Hu and Juan M. Martín-Doñas and David Ditter and Ariel Frank and Antoine Deleforge
and Emmanuel Vincent},
year={2020},
booktitle={Proc. Interspeech},
}
Or on arXiv:
@misc{pariente2020asteroid,
title={Asteroid: the PyTorch-based audio source separation toolkit for researchers},
author={Manuel Pariente and Samuele Cornell and Joris Cosentino and Sunit Sivasankaran and Efthymios Tzinis and Jens Heitkaemper and Michel Olvera and Fabian-Robert Stöter and Mathieu Hu and Juan M. Martín-Doñas and David Ditter and Ariel Frank and Antoine Deleforge and Emmanuel Vincent},
year={2020},
eprint={2005.04132},
archivePrefix={arXiv},
primaryClass={eess.AS}
}