论文标题
定制的神经网络，用于得分信息源分离
Bespoke Neural Networks for Score-Informed Source Separation
论文作者
论文摘要
在本文中，我们引入了一种简单的方法，可以将任意乐器与音频混合物分开。鉴于来自输入混合物的目标仪器的未对准MIDI转录，我们合成了MIDI转录的新混合物，听起来与要分离的混合物相似。这使我们能够创建一个标记的培训集，以培训网络在特定的定制任务上。当该模型应用于原始混合物时，我们证明了该方法可以：1）成功将所需的仪器分开，只能访问未对齐的MIDI，2）单独的任意仪器，3）在现有方法的一小部分中获得结果。我们鼓励读者聆听此处发布的演示：https：//git.io/juu5q。
In this paper, we introduce a simple method that can separate arbitrary musical instruments from an audio mixture. Given an unaligned MIDI transcription for a target instrument from an input mixture, we synthesize new mixtures from the midi transcription that sound similar to the mixture to be separated. This lets us create a labeled training set to train a network on the specific bespoke task. When this model applied to the original mixture, we demonstrate that this method can: 1) successfully separate out the desired instrument with access to only unaligned MIDI, 2) separate arbitrary instruments, and 3) get results in a fraction of the time of existing methods. We encourage readers to listen to the demos posted here: https://git.io/JUu5q.
