Brass Haptics: Comparing Virtual and Physical Trumpets in Extended Realities
Bibliographic record
Abstract
Despite the benefits of learning an instrument, many students drop out early because it can be frustrating for the student, expensive for the caregiver, and loud for the household. Virtual Reality (VR) and Extended Reality (XR) offer the potential to address these challenges by simulating multiple instruments in an engaging and motivating environment through headphones. To assess the potential for commercial VR to augment musical experiences, we used standard VR implementation processes to design four virtual trumpet interfaces: camera-tracking with tracked register selection (two ways), camera-tracking with voice activation, and a controller plus a force-feedback haptic glove. To evaluate these implementations, we created a virtual music classroom that produces audio, notes, and finger pattern guides loaded from a selected Musical Instrument Digital Interface (MIDI) file. We analytically compared these implementations against physical trumpets (both acoustic and MIDI), considering features of ease of use, familiarity, playability, noise, and versatility. The physical trumpets produced the most reliable and familiar experience, and some XR benefits were considered. The camera-based methods were easy to use but lacked tactile feedback. The haptic glove provided improved tracking accuracy and haptic feedback over camera-based methods. Each method was also considered as a proof-of-concept for other instruments, real or imaginary.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".