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Record W1982198449 · doi:10.1162/pres.17.6.527

Virtual Audio Systems

2008· article· en· W1982198449 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

VenuePRESENCE Virtual and Augmented Reality · 2008
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsDalhousie UniversityYork UniversityOntario Tech University
Fundersnot available
KeywordsHeadphonesLoudspeakerComputer scienceVirtual realityHuman–computer interactionPerceptionAmbisonicsPosition (finance)Audio signalSpeech recognitionAcousticsSpeech coding

Abstract

fetched live from OpenAlex

To be immersed in a virtual environment, the user must be presented with plausible sensory input including auditory cues. A virtual (three-dimensional) audio display aims to allow the user to perceive the position of a sound source at an arbitrary position in three-dimensional space despite the fact that the generated sound may be emanating from a fixed number of loudspeakers at fixed positions in space or a pair of headphones. The foundation of virtual audio rests on the development of technology to present auditory signals to the listener's ears so that these signals are perceptually equivalent to those the listener would receive in the environment being simulated. This paper reviews the human perceptual and technical literature relevant to the modeling and generation of accurate audio displays for virtual environments. Approaches to acoustical environment simulation are summarized and the advantages and disadvantages of the various approaches are presented.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.065
GPT teacher head0.292
Teacher spread0.227 · 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