Sound field uncertainty budget for real-ear attenuation at threshold measurement per ANSI S12.6 standards
Bibliographic record
Abstract
In many national and international standards, the attenuation of Hearing Protection Devices is rated according to a psychophysical method called Real-Ear Attenuation at Threshold (REAT), which averages on a group of test-subjects the difference between the open and occluded auditory thresholds. In ANSI S12.6 standard, these REAT tests are conducted in a diffuse sound field in which sound uniformity and directionality are assessed by two objective microphone measurements. While the ANSI S12.6 standard defines these two criteria, it does not link the microphone measurements to the actual variation of sound pressure level at the eardrum that may originate from natural head movements during testing. This presentation examines this issue with detailed measurements conducted in an ANSI S12.6-compliant audiometric booth using an Artificial Test Fixture (ATF). The sound pressure level variations were recorded for movements of the ATF along the three main spatial axes and two rotation planes. From these measured variations and different head movements hypothetical scenarios, various sound field uncertainty budgets were computed. These findings will be discussed in order to eventually include them for uncertainty budget in a revised version of the ANSI S12.6 standard.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".