Comparison of subjective and objective methods for the measurements of hearing protector devices attenuation and occlusion effect
Bibliographic record
Abstract
With the increase popularity of individual fit testing and miniaturization of electronic components, the field-microphone-in-real-ear approach (F-MIRE) is becoming more appealing and well suited for estimating hearing protection devices (HPD) attenuation both in laboratory and in “real world” occupational conditions. The approach utilizes two miniature microphones to simultaneously measure the sound pressure levels in the ear canal under the hearing protector, as well as outside of the protector. In this study, experiments on several human subjects were carried out in order to examine the various factors relating the subjective and objective attenuation values. The subjects were first instrumented on both ears with miniature microphones outside and underneath the protector. They were then asked to go through a series of subjective hearing threshold measurements followed by objective microphone recordings using high level diffuse field broadband noises. Earmuffs, earplugs, and double-protection were tested for each subject, and attenuation values were compared. Additionally, an objective scheme to measure the occlusion effect was developed and tested using subjects’ voice as the excitation and the same microphone setup. Results obtained for the attenuation values as well as the occlusion effect levels are presented and discussed.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".