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Record W3026073252 · doi:10.1121/10.0001257

Characterization of acoustic material at oblique incidence using a spherical microphone array

2020· article· en· W3026073252 on OpenAlexaff
Samuel Dupont, Manuel Melon, Alain Berry

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2020
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsAcousticsMicrophoneSound pressureMicrophone arrayAcoustic impedanceElectrical impedanceComputer scienceAcoustic source localizationMaterials scienceAcoustic wavePhysics

Abstract

fetched live from OpenAlex

of acoustic materials using a spherical microphone array. The first method is based on the spherical ambisonic representation of sound fields to estimate the normal particle velocity and the sound pressure at the surface of the material. The second method uses an optimization process, where the measured sound pressure field is constrained to match a theoretical model. These two methods are compared to two existing methods: the equivalent source approach and the two-microphone measurement technique. The results show a clear advantage of the microphone array methods compared to the classical two-microphone method. The measurements show an accurate reconstruction of surface impedance and absorption coefficient between 120 and 5000 Hz for various sound incidences but also show edge effect perturbations due to the limited size of the tested samples. In addition, two criteria are proposed, one estimating the noise floor level allowing the optimization of the measurement results, the other evaluating the area of accurate impedance reconstruction on the surface of the material.

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 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 score0.512
Threshold uncertainty score0.451

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 studies0.0000.001
Scholarly communication0.0000.000
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 head0.016
GPT teacher head0.238
Teacher spread0.221 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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