Profy通过听感差异定位钢琴练习中的问题片段，辅助针对性改进。
Profy: Interpretable Visualization of Expertise-Dependent Motor Skills Toward Supporting Piano Practice
- 基于听众评分生成弱监督标签，识别专家与新手演奏差异点。
- 在20段业余演奏中，亮点评分与专家标记高度一致（r=0.61，AUC=0.75）。
- 支持逐段回放与聚焦重听，帮助学习者精准定位练习重点。
钢琴演奏质量依赖于微妙的节奏、触键和力度控制，但现有练习反馈多为总结性，难以落实。我们提出Profy，一种弱监督系统，利用从听众综合评分（专家标注 vs. 新手标注）中提取的片段级标签，生成可时间对齐的练习重点提示。收集了73位钢琴家的同步1kHz键位运动与音频数据，使用1,083个有效片段进行建模与评估。模型输出片段级预测结果，并在统一重采样时间基上提供证据分数以供可视化。在21位专家标注的20段业余技巧练习片段中，显示的亮点得分与专家标记高度吻合（皮尔逊相关系数r=0.61，ROC-AUC=0.75），且训练未使用局部标注。相比单一全局评分，Profy通过支持精确定位、循环播放和聚焦重播，帮助学习者决定下一步应重点关注的片段。
原文摘要 · Abstract (English)
The quality of piano performance depends on nuanced timing, articulation, and dynamic control, but practice feedback is often summary-based and hard to act on. We introduce Profy, a weakly supervised system that learns from take-level labels derived from aggregated listener ratings (expert-labeled vs. amateur-labeled) to produce time-aligned highlights for review during piano practice. We collected synchronized 1 kHz key-motion and audio from 73 pianists and used 1,083 valid takes for modeling and evaluation. The model outputs clip-level predictions together with evidence scores on a shared resampled model time base for visualization. On 20 amateur clips from short technique studies annotated by 21 expert pianists, the displayed highlight score aligns with passages that expert pianists marked for review despite training without localized labels (Pearson r=0.61, ROC-AUC 0.75). Rather than summarizing a take with a single global score, Profy helps learners decide where to inspect next by supporting scrubbing, looping, and focused replay of time-localized passages associated with expert-amateur differences.
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