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有效神经网络。具体而言，该卷积网络的骨干结构是多分辨率特征的成功DOwnsampling和重采样（SuDoRMRF）以及通过简单的一维卷积执行的聚合。..

Sudo rm -rf: Efficient Networks for Universal Audio Source Separation

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.