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Record W4313071921 · doi:10.1051/aacus/2022054

Characterization of acoustic materials at arbitrary incidence angle using sound field synthesis

2022· article· en· W4313071921 on OpenAlexaff
Samuel Dupont, Maryna Sanalatii, Manuel Melon, Olivier Robin, Alain Berry, Jean‐Christophe Le Roux

Bibliographic record

VenueActa Acustica · 2022
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de Sherbrooke
FundersCentre National de la Recherche ScientifiqueAgence Nationale de la Recherche
KeywordsAcousticsLoudspeakerMicrophoneDirectional soundReverberation roomAcoustic impedanceElectromagnetic reverberation chamberReverberationPlane waveElectrical impedanceSound pressureSound powerAbsorption (acoustics)Parametric arrayAcoustic waveSoundproofingPhysicsOpticsSound (geography)Ultrasonic sensor

Abstract

fetched live from OpenAlex

Standardized methods for measuring sound absorption such as the impedance tube and reverberation chamber methods are limited to normal or diffuse incidence, respectively. Two research axes have been generally followed in the literature to develop alternative techniques, the first one focusing on the measurement part, that is from the two-microphone technique to the use of microphone arrays or pressure-velocity sensors. The second axis focuses on the excitation part with for instance the use of sound field synthesis techniques. Since acoustic impedance and sound absorption coefficient of materials are classically defined under normal and oblique plane wave excitation, synthesizing an “ideal” plane wave using a loudspeaker array would allow measuring these acoustics quantities using a simple microphone pair. In this article, the effect of the different parameters of a loudspeaker array on acoustic plane waves reproduction on a material’s surface is first numerically studied. Then, numerical and experimental results for the estimation of both impedance and absorption coefficients are reported. These results show that sound field synthesis allows to characterize a material for arbitrary incidence angles over a wide frequency range, thus offering an alternative method to standard techniques and an improvement over existing works.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.020
GPT teacher head0.243
Teacher spread0.222 · 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.

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
Published2022
Admission routes1
Has abstractyes

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