MétaCan
Menu
Back to cohort
Record W4400285085 · doi:10.1121/10.0026818

Designing an acoustical test fixture to evaluate the objective occlusion effect

2024· article· en· W4400285085 on OpenAlexaff
Olivier Doutres, Yu Luan, Marc-Olivier Cyr-Desroches, Kévin Carillo, Robin Richert, Franck Sgard

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2024
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travailÉcole de Technologie Supérieure
Fundersnot available
KeywordsFixtureTest fixtureTest (biology)Computer scienceEngineeringAcousticsReliability engineeringMechanical engineeringGeologyPhysics

Abstract

fetched live from OpenAlex

Earplugs are commonly used to prevent noise-induced hearing loss. However, their effectiveness is often hindered by the discomfort they cause, impacting consistent and correct use. An important acoustical discomfort, known as the occlusion effect, arises from an increased perception of bone-conducted physiological sounds (such as one's own voice, breathing, and chewing) when the ear canal is occluded. To objectively assess this discomfort, the study proposes the use of an acoustical test fixture (ATF) that avoids direct measurements on human participants. The ATF employs an anatomically realistic truncated outer ear, incorporating soft tissues, cartilage, and bone components to replicate the outer ear's bone conduction path, crucial for occlusion effect assessments. The study demonstrates that the proposed ATF can replicate key effects observed in objective ccclusion effect (OE) measurements on human participants, including significant OE at low frequencies diminishing with increasing frequency, reduction of OE with greater insertion depths, and distinctions among various earplug types—especially noticeable at deeper insertions. Furthermore, a computationally efficient finite element method-based virtual tester for the ATF is developed and validated.

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)0.003
metaresearch head score (Gemma)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.949
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.012
GPT teacher head0.288
Teacher spread0.276 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicAcoustic Wave Phenomena ResearchFrench-language works237,207