Vrmin: Using Mixed Reality To Augment The Theremin For Musical Tutoring
Bibliographic record
Abstract
The recent resurgence of Virtual Reality (VR) technologies provide new platforms for augmenting traditional music instruments. Instrument augmentation is a common approach for designing new interfaces for musical expression, as shown through hyperinstrument research. New visual affordances present in VR give designers new methods for augmenting instruments to extend not only their expressivity, but also their capabilities for computer assisted tutoring. In this work, we present VRMin, a mobile Mixed Reality (MR) application for augmenting a physical theremin, with an immersive virtual environment (VE), for real time computer assisted tutoring. We augment a physical theremin with 3D visual cues to indicate correct hand positioning for performing given notes and volumes. The physical theremin acts as a domain specific controller for the resulting MR environment. The initial effectiveness of this approach is measured by analyzing a performer's hand position while training with and without the VRMin. We also evaluate the usability of the interface using heuristic evaluation based on a newly proposed set of guidelines designed for VR musical environments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".