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Record W2793896580 · doi:10.5281/zenodo.1176205

Vrmin: Using Mixed Reality To Augment The Theremin For Musical Tutoring

2017· article· en· W2793896580 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2017
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsAugmentMusicalComputer scienceMixed realityHuman–computer interactionAugmented realityVisual artsArtLinguistics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0070.000
Scholarly communication0.0020.000
Open science0.0040.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.136
GPT teacher head0.327
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it