Piano fingering shapes how a passage can be played, yet it is difficult to label after a performance. An annotator must decide which finger produced each note while reconciling the score, timing, video, and hand motion. We present PiAnnotate, a web-based pipeline for adding expert fingering annotations to the FurElise performance dataset. The tool brings together a piano-roll view, performance video, and a 3D MANO hand mesh so that reviewers can inspect each assignment in musical and physical context. Rather than storing only the final answer, PiAnnotate keeps paired rule-based and human-edited fingering tracks. These paired tracks make the annotation history auditable by showing where a geometric rule was sufficient, where experts intervened, and how labels changed across review passes. As a final diagnostic, we train a small Transformer probe on the paired tracks. The probe improves on the rule baseline on held-out pieces while remaining conservative about changing labels that were already correct, suggesting that the edited labels contain learnable structure rather than only isolated fixes.

翻译：钢琴指法决定了乐段的可演奏方式，然而在演奏后标注指法却十分困难。标注者需在协调乐谱、时序、视频与手部运动的同时，判定每个音符由哪根手指弹奏。我们提出PiAnnotate——一个基于网络的流水线工具，用于为FurElise演奏数据集添加专家级指法标注。该工具整合了钢琴卷帘视图、演奏视频及3D MANO手部网格，使评审者能在音乐与物理情境中检查每项标注分配。PiAnnotate不仅存储最终结果，还保留配对的基于规则与人工编辑的指法轨道。这些配对轨道通过展示几何规则何时足以生效、专家在何处进行了干预以及标注在多次评审中如何变化，使标注历史可审计。作为最终诊断，我们在配对轨道上训练了一个小型Transformer探针。该探针在保留样本上优于规则基线，同时对于已正确的标注保持保守修改，表明编辑后的标注包含可学习结构而非孤立的修正。