论文标题
所有人都一无所有：通过桥接网络改善音乐分离
All for One and One for All: Improving Music Separation by Bridging Networks
论文作者
论文摘要
本文提出了通过深神经网络（DNN）（即多域损失（MDL）和两个组合方案）进行音乐分离的几种改进。首先，通过使用MDL，我们利用音频信号的频率和时间域表示。接下来，我们通过共同考虑工具之间的关系。一方面，我们通过修改网络体系结构并引入交叉网络结构来做到这一点。另一方面，我们考虑使用新组合损失（CL）来考虑仪器估计的组合。 MDL和CL可以轻松地应用于许多现有的基于DNN的分离方法，因为它们仅仅是损失函数，仅在训练过程中使用，并且不会影响推理步骤。实验结果表明，可以利用我们的上述方案来改善开放式Unmix（UMX）的性能（UMX）是一个著名和最先进的开源库，可以改善音乐分离。我们对UMX的修改与本文一起开源。
This paper proposes several improvements for music separation with deep neural networks (DNNs), namely a multi-domain loss (MDL) and two combination schemes. First, by using MDL we take advantage of the frequency and time domain representation of audio signals. Next, we utilize the relationship among instruments by jointly considering them. We do this on the one hand by modifying the network architecture and introducing a CrossNet structure. On the other hand, we consider combinations of instrument estimates by using a new combination loss (CL). MDL and CL can easily be applied to many existing DNN-based separation methods as they are merely loss functions which are only used during training and which do not affect the inference step. Experimental results show that the performance of Open-Unmix (UMX), a well-known and state-of-the-art open source library for music separation, can be improved by utilizing our above schemes. Our modifications of UMX are open-sourced together with this paper.
