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Record W2921399382 · doi:10.1145/3294109.3301256

Physically Colliding with Music

2019· article· en· W2921399382 on OpenAlexaff
Raul Altosaar, Adam Tindale, Judith Doyle

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsOntario College of Art and Design
Fundersnot available
KeywordsComputer scienceAffordanceHuman–computer interactionExpansiveVirtual realityImmersion (mathematics)Performing artsMusicalInterface (matter)ReconfigurabilityGestureComputer vision

Abstract

fetched live from OpenAlex

A Very Real Looper (AVRL) is an audio-only virtual reality (VR) interface inside of which a performer triggers and controls music through full-body movement. Contrary to how musical interfaces in VR are normally used, a performer using AVRL is not disconnected from their surrounding environment through immersion, nor is their body restrained by a head-mounted display. Rather, AVRL utilizes two VR sensors and the Unity game engine to map virtual musical sounds onto physical objects in the real world. These objects help the performer locate the sounds. Using two handheld VR controllers, these sounds can be triggered, looped, acoustically affected, or repositioned in space. AVRL thus combines the affordances of the physical world and a VR system with the reconfigurability of a game engine. This integration results in an expansive and augmented performance environment that facilitates full-body musical interactions.

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.213
Teacher spread0.201 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2019
Admission routes1
Has abstractyes

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