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Record W3215148866

Adapting to a virtual work using immersive audio with Copelli-AI

2021· article· en· W3215148866 on OpenAlexaffabout
Dylan Cave

Bibliographic record

VenueStudent Research Proceedings · 2021
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsMacEwan University
Fundersnot available
KeywordsPerformative utteranceVisual artsMusicalFormative assessmentPerforming artsVirtual realityMultimediaComputer scienceImmersion (mathematics)Human–computer interactionAestheticsArtPsychology
DOInot available

Abstract

fetched live from OpenAlex

Exploring modern performative art through immersive audio in an ever changing virtual world. This research is intended  to present how we may use technology in a creative way with artistic intent to help convey metaphorical content within the art. Working with the musical score in direct conjunction with per formative art, I examine artistic intent and work directly with the creative team to augment their vision using immersive binaural audio mixing techniques.  The composer and I will discuss challenges we faced adapting the score for immersion and what sort of things we managed to achieve despite all the difficulties we faced in the process. We will present a  live demonstration of one movement from the upcoming modern immersive ballet Copelli-AI that will premier at Edmonton Fringe Festival in 2021. Department: Music Faculty Mentor: Dr. Bill Richards

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.115
GPT teacher head0.402
Teacher spread0.287 · 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 designQualitative
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

Citations0
Published2021
Admission routes2
Has abstractyes

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