用图像色化网络修复钢琴MIDI音符强弱，让电子音乐更自然。
Filling MIDI Velocity using U-Net Image Colorizer
- 将MIDI数据视为图像，用U-Net结合窗口注意力建模
- 在MAESTRO v3和SMD上优于已有方法，量化与听感双提升
- 适合音乐生成、自动配器研究者参考
现代音乐制作常用MIDI存储乐曲，但软件生成的MIDI常缺少人类演奏的表现力，尤其是音符力度（velocity）参数未定义，仅保留默认平值。本文提出通过回归模型预测并填充这一参数，以增强音乐表现力。我们引入图像着色中常用的U-Net架构，将MIDI数据转化为图像形式，设计窗口注意力机制与自定义损失函数，解决数据稀疏问题。实验仅限于钢琴数据，在MAESTRO v3与SMD数据集上，该方法在定量指标和主观听感测试中均优于现有方法。
原文摘要 · Abstract (English)
Modern music producers commonly use MIDI (Musical Instrument Digital Interface) to store their musical compositions. However, MIDI files created with digital software may lack the expressive characteristics of human performances, essentially leaving the velocity parameter - a control for note loudness - undefined, which defaults to a flat value. The task of filling MIDI velocity is termed MIDI velocity prediction, which uses regression models to enhance music expressiveness by adjusting only this parameter. In this paper, we introduce the U-Net, a widely adopted architecture in image colorization, to this task. By conceptualizing MIDI data as images, we adopt window attention and develop a custom loss function to address the sparsity of MIDI-converted images. Current dataset availability restricts our experiments to piano data. Evaluated on the MAESTRO v3 and SMD datasets, our proposed method for filling MIDI velocity outperforms previous approaches in both quantitative metrics and qualitative listening tests.
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