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.

本文提出了一种高效的神经网络用于端到端通用音频源分离，使用了连续下采样和多分辨率特征重取样（SuDoRMRF）的骨干结构，以及通过简单的一维卷积来执行特征聚合，从而实现了高质量的音频源分离，同时具有受限的浮点运算、内存需求、参数数量和延迟。我们在语音和环境声音分离数据集上的实验表明，SuDoRMRF 可与多种需要更高计算资源的最先进方法相媲美，甚至表现更优越。