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Record W4380082204 · doi:10.1051/aacus/2023021

Measurement of the diffuse field sound absorption using a sound field synthesis method

2023· article· en· W4380082204 on OpenAlexaff
Samuel Dupont, Maryna Sanalatii, Manuel Melon, Olivier Robin, Alain Berry, Jean‐Christophe Le Roux

Bibliographic record

VenueActa Acustica · 2023
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de Sherbrooke
FundersCentre National de la Recherche Scientifique
KeywordsReverberation roomNoise reduction coefficientAcousticsElectromagnetic reverberation chamberMicrophoneAbsorption (acoustics)ReverberationLoudspeakerArchitectural acousticsAttenuation coefficientField (mathematics)OpticsAngle of incidence (optics)Sound intensity probeMaterials scienceRoom acousticsRange (aeronautics)Critical distanceComputational physicsPhysicsSound powerSound (geography)Mathematics

Abstract

fetched live from OpenAlex

A method for measuring the diffuse field sound absorption coefficient of a material using sound field synthesis is proposed. A planar loudspeaker array is first used to generate acoustic plane waves with variable incidence angle on the surface of a material under test. Using a two-microphone probe positioned closely to the sample’s surface, the angle-dependent sound absorption coefficients are then estimated. Finally, the diffuse field absorption coefficient is computed following Paris formula. Numerical simulations are used to evaluate the respective effects of the maximum incidence angle value and the number of individual incidence angles that are required for a robust calculation of the diffuse sound field absorption. Measurements are conducted on three different materials and compared with either simulation results obtained using the Johnson-Champoux-Allard theory, or with measurement results obtained using the standard reverberation chamber method. For all considered materials and over a wide frequency range, the proposed method leads to results that are in better agreement with theoretical predictions than those obtained using standardized methods.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.065
GPT teacher head0.309
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2023
Admission routes1
Has abstractyes

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