用掩码扩散模型提升节拍追踪一致性，减少错误节拍与乱变速度。
Masked diffusion enables coherent beat tracking
- 引入掩码扩散机制，显式建模多个可能的节拍网格。
- 在标准掩码扩散基础上改进三项策略，提升推断稳定性。
- 适合需要高精度节拍分析的音乐处理任务，如自动编曲与伴奏生成。
当前节拍追踪神经网络会产生无效输出，如连续的强拍和不规则的速度变化，即使训练数据中不存在此类现象。尽管可通过复杂的后处理缓解问题，但其根本原因仍不清楚。我们假设问题源于对多个合理节拍网格建模不足，导致模型产生矛盾解释的混合。为此，提出一种掩码扩散方法，通过迭代推理构建一致预测。设计了三项改进：训练与推理时对节拍和强拍独立掩码、平衡的掩码调度器用于推理、跨推理步骤的峰值提取。该方法有效减少异常行为，并提升节拍追踪性能。
原文摘要 · Abstract (English)
Current neural networks for beat tracking generate invalid outputs, such as consecutive downbeats and erratic tempo changes, even when these are not present in the training data. Heavy post-processing techniques can alleviate these problems, but the original cause of this inconsistent behaviour remains unknown. We hypothesise that it stems from inadequate modelling of multiple plausible output beat grids, resulting in an invalid mixture of competing interpretations. We propose a masked diffusion approach that properly models multiple outputs and enables the model to build coherent predictions through iterative inference. We devise three modifications to standard masked diffusion that enable its application to beat tracking: independent masking of beats and downbeats during training and inference, a balanced masking scheduler for inference, and peak-picking across inference steps. Our approach reduces erratic behaviours and improves beat-tracking performance.
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