Demo Data from SIGIR 2026 / reviewers in the wild / expert
Jingjing Tang 0002
dblp:138/5240-2
· DBLP profile ↗
2ranked-venue papers
2as first author
2since 2021 · last 2026
0009-0001-1517-667XORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Audio and music processing · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Learning and educational technologies · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Audio and music processing › music analysis
expressive performance analysis |
1.0 | 1 | 2026 | Visualising Pianists' Touch: Transcribing Expressive Piano Performance from Audio to Piano Key Motion · CHI 2026 |
Methods — techniques the papers use, named apart from their topics
user study · 2.0audio-to-motion transcription · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Visualising Pianists' Touch: Transcribing Expressive Piano Performance from Audio to Piano Key MotionabstractDetailed measurements of piano key motion capture touch, timing, and dynamic control, providing crucial performance insights. Such expressive gestures are overlooked in MIDI, which only records pitch onset, duration, and velocity. Here, we introduce a novel transcription technique that directly maps audio from expressive piano performance to continuous piano key motion. User studies reveal a preference to the transcribed key motion trajectories over MIDI in representing sound, and over 80% accuracy in matching transcribed trajectories to audio from contrasting piano expressions. Follow-up interviews further indicate that the visualised trajectories can reveal subtle performance nuances and provide actionable guidance for both teaching and practice. An interface example for pedagogy and performance analysis utilising our technique is also illustrated. By providing a physically grounded performance representation that musicians can interpret and act upon, this work establishes a foundation for future interactive tools in music pedagogy, performance feedback, and embodied musical learning. Jingjing Tang 0002, Shinichi Furuya, Hayato Nishioka, Momoko Shioki, Geraint A. Wiggins, György Fazekas, Vincent K. M. Cheung |
CHI | 1 |
| 2025 | Towards An Integrated Approach for Expressive Piano Performance Synthesis from Music ScoresabstractThis paper presents an integrated system that transforms symbolic music scores into expressive piano performance audio. By combining a Transformer-based Expressive Performance Rendering (EPR) model with a fine-tuned neural MIDI synthesiser, our approach directly generates expressive audio performances from score inputs. To the best of our knowledge, this is the first system to offer a streamlined method for converting score MIDI files lacking expression control into rich, expressive piano performances. We conducted experiments using subsets of the ATEPP dataset, evaluating the system with both objective metrics and subjective listening tests. Our system not only accurately reconstructs human-like expressiveness, but also captures the acoustic ambience of environments such as concert halls and recording studios. Additionally, the proposed system demonstrates its ability to achieve musical expressiveness while ensuring good audio quality in its outputs. Jingjing Tang 0002, Erica Cooper, Xin Wang 0037, Junichi Yamagishi, György Fazekas |
ICASSP | 1 |