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Record W2047967926 · doi:10.1080/2151237x.2011.619888

A GPU-Based Method to Approximate Acoustical Reflectivity

2011· article· en· W2047967926 on OpenAlexafffund
Brent Cowan, Bill Kapralos

Bibliographic record

VenueJournal of Graphics GPU and Game Tools · 2011
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversity of Ontario Institute of Technology
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsReverberationComputer scienceMeasure (data warehouse)ReflectivityEnergy (signal processing)HeuristicCode (set theory)AcousticsSource codeFraction (chemistry)Computer graphics (images)AlgorithmArtificial intelligenceOpticsMathematicsPhysicsStatistics

Abstract

fetched live from OpenAlex

Abstract Here we present a GPU-based heuristic method for approximating the size and reflectivity of a room relative to a given sound source origin that can potentially provide interactive response for the appropriate selections of geometry and hardware. The method calculates the average distance to the nearest surfaces in the environment and an approximate measure of the fraction of sound energy that would be reflected from those surfaces. Both of these values are then used to drive the reverberation effects of an audio API. The method can potentially provide interactive response for the appropriate selections of geometry and hardware. Source code is available online at address provided at the end of this paper.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.784
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.072
GPT teacher head0.314
Teacher spread0.242 · 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 designSimulation or modeling
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

Citations5
Published2011
Admission routes2
Has abstractyes

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