Precise note-level annotations are critical for training automatic music transcription (AMT) systems, in particular note-onset labels, which form a core component of many recent AMT systems. However, high-quality annotations for real-world recordings are scarce. Sequence-level score--audio alignment methods such as dynamic time warping provide only coarse correspondence, making a local refinement step necessary. This refinement step, known as snapping, adjusts aligned score onsets using peaks in a neural onset posteriorgram and often determines whether weakly aligned score--audio pairs become usable training data at all. Despite its practical importance, snapping is typically treated as a simple post-processing heuristic and implemented with greedy local decisions. We present a systematic analysis of snapping strategies for training instrument-agnostic transcribers, demonstrating that snapping is essential for learning from weakly aligned data. Building on this, we formulate snapping as a per-pitch assignment problem and solve it via bipartite graph matching, yielding context-aware onset decisions under overlapping refinement windows and uncertain initial alignments. Extensive cross-dataset experiments across piano, chamber, and orchestral recordings show improved onset alignment and transcription accuracy over greedy snapping, with gains increasing for wider snapping windows and coarser initial alignments. Qualitative examples are provided on our project page: https://abhirupsaha8.github.io

翻译：精确的音符级标注对训练自动音乐转录（AMT）系统至关重要，尤其是构成当前众多AMT系统核心组件的音符起始点标签。然而，真实录音的高质量标注数据极为稀少。动态时间规整等序列级乐谱-音频对齐方法仅能提供粗略对应关系，因此需要局部精调步骤。该精调步骤（即"匹配"）通过利用神经起始点后验图中的峰值来调整已对齐的乐谱起始点，往往决定了弱对齐的乐谱-音频配对能否成为可用的训练数据。尽管具有重要实践意义，匹配通常仅被视为简单的后处理启发式方法，并通过贪婪局部决策实现。本文对训练乐器无关转录器的匹配策略进行了系统性分析，证明匹配是从弱对齐数据中学习的必要条件。在此基础上，我们将匹配建模为逐音高分配问题，并通过二分图匹配求解，从而在重叠精调窗口与不确定性初始对齐条件下实现具备语境感知能力的起始点决策。针对钢琴、室内乐与管弦乐录音的跨数据集实验表明，相比贪婪匹配方法，本方法在起始点对齐精度与转录准确度上均有提升，且随着匹配窗口增大与初始对齐精度下降，性能增益更为显著。定性示例详见项目页面：https://abhirupsaha8.github.io