MétaCan
Menu
Back to cohort
Record W3153996027

Localization of Moving Sound Sources In Virtual Reality Technology

2017· article· en· W3153996027 on OpenAlexaff
Lukas Grasse, Scott A. Stone, Matthew S. Tata

Bibliographic record

VenueURSCA Proceedings · 2017
Typearticle
Languageen
FieldNeuroscience
TopicCognitive Science and Education Research
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsHeadsetHeadphonesVirtual realityIllusionSound (geography)AmbiguityPerceptionComputer scienceSound localizationAugmented realityComputer visionAcousticsHuman–computer interactionPsychologyPhysicsCognitive psychologyTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

To localize sounds, the brain uses differences in sound phase and amplitude between the ears; however these cues can lead to ambiguity in localization. In 1940, Hans Wallach discovered an illusion in which a sound source appears stationary when it moves at twice the angular velocity of head rotation. Recent advances in virtual-reality (VR) technology have allowed us to recreate this illusion using headphones and a VR headset. We are using this system to investigate how the brain handles ambiguity in perception of dynamic auditory scenes. We can also investigate practical problems that may arise when simulating moving sound sources in virtual or augmented reality situations. * Indicates faculty mentor

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)0.005
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 score0.653
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.084
GPT teacher head0.375
Teacher spread0.291 · 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 designBench or experimental
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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueURSCA ProceedingsSame topicCognitive Science and Education ResearchFrench-language works237,207