Asteroid: the PyTorch-based audio source separation toolkit for researchers
Audio and Speech Processing
2020-05-11 v1 Sound
Abstract
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 .
Cite
@article{arxiv.2005.04132,
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},
journal= {arXiv preprint arXiv:2005.04132},
year = {2020}
}
Comments
Submitted to Interspeech 2020