用最优传输方法优化钢琴乐谱转录，更符合听觉感知。
A Distribution Matching Approach to Neural Piano Transcription with Optimal Transport
- 将钢琴转录建模为最优传输问题，而非传统分类任务。
- 在MAESTRO数据集上达到当前最佳的音符起始点检测效果。
- 适用于现有模型，提升对时间错位的鲁棒性，适合音乐信号处理研究者。
本文提出一种新范式，将自动钢琴转录（APT）形式化为最优传输（OT）问题，而非传统的帧级多标签二分类问题。该方法通过最小化预测音符事件分布与真实分布之间的时间-频率传输成本，实现对时间错位的容忍，从而获得更符合听觉感知的优化结果。同时，我们设计了一种含谐波感知注意力机制的卷积循环神经网络（CRNN），以捕捉音乐中的时频依赖关系。在MAESTRO数据集上的实验表明，该方法在音符起始点检测上达到了当前最优性能。此外，验证了OT损失函数在适配现有模型时的通用性。
原文摘要 · Abstract (English)
This paper describes a novel paradigm that formalizes automatic piano transcription (APT) as an optimal transport (OT) problem, not as a frame-level multi-label binary classification problem. Our method learns to minimize the cost of transporting a predicted distribution of note events to the ground-truth distribution over time and frequency. The OT loss can thus accommodate temporal misalignment, leading to perceptually relevant optimization. We also propose a convolutional recurrent neural network (CRNN) with a harmonics-aware attention mechanism to capture the spectro-temporal dependencies inherent in music.Our experiments using the MAESTRO dataset showed that our method attained a state-of-the-art performance in onset detection. We confirmed the versatility of the OT loss in application to existing models.
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