[Conference] A paper by Jun Rekimoto and Shinichi Furuya et al. has been presented at ACM Designing Interactive Systems (DIS 2026)

A paper by Jun Rekimoto and Shinichi Furuya et al. has been presented at ACM Designing Interactive Systems (DIS 2026).

Title : Interpretable Visualization of Expertise-Dependent Motor Skills Toward Supporting Piano Practice
Authors : Kazuki Kawamura, Fujiki Nakamura, Hayato Nishioka, Momoko Shioki, Shinichi Furuya, Jun Rekimoto
ACM Designing Interactive Systems (DIS 2026), Singapore
https://dl.acm.org/doi/10.1145/3800645.3812903

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 expertise labels (Expert vs. Amateur) to produce time-aligned highlights that localize candidate passages for review during piano practice. Using synchronized 1 kHz key-motion and audio from 80 pianists, the model outputs clip-level predictions with per-frame evidence scores for visualization. On 20 amateur clips from short technique studies annotated by 21 expert pianists, the displayed highlight score aligns with expert-marked problematic passages 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-local passages associated with expert-amateur differences.