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Record W2120117366 · doi:10.4103/1463-1741.77214

Development and validation of a field microphone-in-real-ear approach for measuring hearing protector attenuation

2011· article· en· W2120117366 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNoise and Health · 2011
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsMicrophoneAttenuationHearing protectionAcousticsComputer scienceNoise (video)Ear canalAudiologyPhysicsOpticsHearing lossArtificial intelligenceSound pressureMedicine

Abstract

fetched live from OpenAlex

Numerous studies have shown that the reliability of using laboratory measurements to predict individual or even group hearing protector attenuation for occupationally exposed workers is quite poor. This makes it difficult to properly assign hearing protectors when one wishes to closely match attenuation to actual exposure. An alternative is the use of field-measurement methods, a number of which have been proposed and are beginning to be implemented. We examine one of those methods, namely the field microphone-in-real-ear (F-MIRE) approach in which a dual-element microphone probe is used to measure noise reduction by quickly sampling the difference in noise levels outside and under an earplug, with appropriate adjustments to predict real-ear attenuation at threshold (REAT). We report on experiments that validate the ability of one commercially available F-MIRE device to predict the REAT of an earplug fitted identically for two tests. Results are reported on a representative roll-down foam earplug, stemmed-style pod plug, and pre-molded earplug, demonstrating that the 95% confidence level of the Personal Attenuation Rating (PAR) as a function of the number of fits varies from ± 4.4 dB to ± 6.3 dB, depending on the plug type, which can be reduced to ± 3.1 dB to ± 4.5 dB with a single repeat measurement. The added measurement improves precision substantially. However, the largest portion of the error is due to the user's fitting variability and not the uncertainty of the measurement system. Further we evaluated the inherent uncertainty of F-MIRE vs. the putative "gold standard" REAT procedures finding, that F-MIRE measurement uncertainty is less than one-half that of REAT at most test frequencies. An American National Standards Institute (ANSI) working group (S12/WG11) is currently involved in developing methods similar to those in this paper so that procedures for evaluating and reporting uncertainty on all types of field attenuation measurement systems can be standardized. We conclude that the hearing conservationist now has available a portable, convenient, quick, and easy-to-use system that can improve training and motivation of employees, assign hearing protection devices based on noise exposures, and address other management and compliance issues.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score0.288

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)0.0000.000

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 head0.148
GPT teacher head0.317
Teacher spread0.169 · 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