论文标题
Sudo RM -RF：通用音频源分离的有效网络
Sudo rm -rf: Efficient Networks for Universal Audio Source Separation
论文作者
论文摘要
在本文中，我们提出了一个有效的神经网络，用于端到端通用音频源分离。具体而言，该卷积网络的骨干结构是多分辨率特征（SudOrmrf）的连续下采样和重新采样以及通过简单的一维卷积执行的聚合。通过这种方式，我们能够获得高质量的音频源分离，浮点操作数量有限，内存需求，参数数量和延迟。我们对语音和环境声音分离数据集的实验表明，SudorMRF的性能相当，甚至超过了各种最新方法，具有明显更高的计算资源需求。
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.
