Designing a Multimodal Viewer for Piano Performance Analysis -- a Pedagogy-First Approach
September 10, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Joonhyung Bae, Hyeyoon Cho, Kirak Kim, Dawon Park, Taegyun Kwon, Yoon-Seok Choi, Hyeon Hur, Shigeru Kai, Yohei Wada, Satoshi Obata, Akira Maezawa, Jaebum Park, Jonghwa Park, Juhan Nam
arXiv ID
2511.21693
Category
cs.MM: Multimedia
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Abstract instructions in piano education, such as "raise your wrist" and "relax your tension," lead to varying interpretations among learners, preventing instructors from effectively conveying their intended pedagogical guidance. To address this problem, this study conducted systematic interviews with a piano professor with 18 years teaching experience, and two researchers derived seven core need groups through cross-validation. Based on these findings, we developed a web-based dashboard prototype integrating video, motion capture, and musical scores, enabling instructors to provide concrete, visual feedback instead of relying solely on abstract verbal instructions. Technical feasibility was validated through 109 performance datasets.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Multimedia
π
π
Old Age
R.I.P.
π»
Ghosted
Viewport-Adaptive Navigable 360-Degree Video Delivery
π
π
The Cartographer
A Comprehensive Survey on Cross-modal Retrieval
π
π
The Cartographer
An Overview of Cross-media Retrieval: Concepts, Methodologies, Benchmarks and Challenges
R.I.P.
π»
Ghosted
A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding
R.I.P.
π»
Ghosted
Video Generation From Text
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted