In this paper, we present an efficient neural network for end-to-end general purpose audio source separation. Specifically, the backbone structure of this convolutional network is the SUccessive DOwnsampling and Resampling of Multi-Resolution Features (SuDoRMRF) as well as their aggregation which is performed through simple one-dimensional convolutions. In this way, we are able to obtain high quality audio source separation with limited number of floating point operations, memory requirements, number of parameters and latency. Our experiments on both speech and environmental sound separation datasets show that SuDoRMRF performs comparably and even surpasses various state-of-the-art approaches with significantly higher computational resource requirements.

中文翻译：

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Sudo rm -rf：通用音频源分离的高效网络

在本文中，我们提出了一种用于端到端通用音频源分离的高效神经网络。具体来说，这个卷积网络的主干结构是多分辨率特征的 SUccessive DOwnsampling 和 Resampling (SuDoRMRF) 以及它们通过简单的一维卷积执行的聚合。通过这种方式，我们能够在有限数量的浮点运算、内存要求、参数数量和延迟的情况下获得高质量的音频源分离。我们在语音和环境声音分离数据集上的实验表明，SuDoRMRF 的性能相当，甚至超过了具有更高计算资源需求的各种最先进的方法。