Development and validation of a field microphone-in-real-ear approach for measuring hearing protector attenuation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".