PiAnnotate: A Web Annotation Tool for Piano Fingering, with a Diagnostic Probe
For researchers in piano performance analysis and music AI, this tool provides auditable fingering annotations and demonstrates that human-edited labels contain learnable structure.
PiAnnotate is a web tool for annotating piano fingering in the FurElise dataset, combining piano-roll, video, and 3D hand mesh. A Transformer probe trained on paired rule-based and human-edited tracks improves over the rule baseline on held-out pieces while conservatively preserving correct labels.
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.