Asteroid: The PyTorch-based audio source separation toolkit for researchers

Manuel Pariente, Samuele Cornell, Joris Cosentino, Sunit Sivasankaran, Efthymios Tzinis, Jens Heitkaemper, Michel Olvera, Fabian Robert Stöter, Mathieu Hu, Juan M. Martín-Doñas, David Ditter, Ariel Frank, Antoine Deleforge, Emmanuel Vincent

Research output: Contribution to journalConference articlepeer-review

98 Scopus citations

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 github.com/mpariente/asteroid.

Original languageEnglish
Pages (from-to)2637-2641
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume2020-October
DOIs
StatePublished - 2020
Externally publishedYes
Event21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020 - Shanghai, China
Duration: 25 Oct 202029 Oct 2020

Bibliographical note

Publisher Copyright:
© 2020 International Speech Communication Association. All rights reserved.

Funding

Experiments presented in this paper were partially carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr). High Performance Computing resources were partially provided by the EX-PLOR centre hosted by the University de Lorraine.

FundersFunder number
RENATER
University de Lorraine
Centre National de la Recherche Scientifique
several Universities

    Keywords

    • End-to-end
    • Open-source software
    • Source separation
    • Speech enhancement

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