论文标题
小行星：研究人员的基于Pytorch的音频源分离工具包
Asteroid: the PyTorch-based audio source separation toolkit for researchers
论文作者
论文摘要
本文描述了小行星，这是针对研究人员的基于Pytorch的音频源分离工具包。受到最成功的神经源分离系统的启发，它提供了建立此类系统所需的所有神经构建块。为了提高可重复性，还提供了常见音频源分离数据集上的Kaldi式食谱。本文描述了小行星的软件体系结构及其最重要的功能。通过显示通过小行星食谱获得的实验结果，我们表明我们的实现至少与参考论文中报告的大多数结果相当。该工具包可在https://github.com/mpariente/asteroid上公开获得。
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 .
