Asteroid: the PyTorch-based audio source separation toolkit for researchers
This paper describes Asteroid, the PyTorch-based audio source separation toolkit for researchers. Inspired by the most successful neural source separation systems, it provides all neural building blocks required to build such a system. To improve reproducibility, Kaldi-style recipes on common audio...
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| Veröffentlicht in: | arXiv.org 2020-05 |
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| creator | Pariente, Manuel Cornell, Samuele Cosentino, Joris Sivasankaran, Sunit Tzinis, Efthymios Heitkaemper, Jens Olvera, Michel Fabian-Robert Stöter Hu, Mathieu Martín-Doñas, Juan M Ditter, David Frank, Ariel Deleforge, Antoine Vincent, Emmanuel |
| description | This paper describes Asteroid, the PyTorch-based audio source separation toolkit for researchers. Inspired by the most successful neural source separation systems, it provides all neural building blocks required to build such a system. To improve reproducibility, Kaldi-style recipes on common audio source separation datasets are also provided. This paper describes the software architecture of Asteroid and its most important features. By showing experimental results obtained with Asteroid's recipes, we show that our implementations are at least on par with most results reported in reference papers. The toolkit is publicly available at https://github.com/mpariente/asteroid . |
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| identifier | EISSN: 2331-8422 |
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| issn | 2331-8422 |
| language | eng |
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| subjects | Asteroids Computer architecture Researchers Separation Toolkits Yard waste |
| title | Asteroid: the PyTorch-based audio source separation toolkit for researchers |
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