Effects of the Hardness of Acoustic Test Fixtures' Ears on the Evaluation of Earplug’s Direct Transmissions Facing High-Level Impulse Noises.
Bibliographic record
Abstract
Apart from secondary propagation paths, the performance of Hearing Protection Devices (HPD) facing high-level impulse noises is also limited by the acoustic transmission through the protector. In the earplug's case, part of these transmissions can be described as the protection's displacement along the ear canal axis. It has been demonstrated that the peak amplitude of this displacement is directly proportional to the peak acoustic pressure measured behind the protector when Acoustic Test Fixtures (ATF) are used. In order to better understand these results and the consequences for the human ear, it is now required to evaluate the influence of the material properties involved in the ATF ear canal and HPD coupling. For this purpose, a laser-vibrometer is used to measure the earplug's peak displacements when the latter is inserted in two ATF ear canals of different materials (soft and medium hardness). The impulse waves employed to excite the earplug ranged from 150 dB to 180 dB peak. The experiment highlights that for the same earplug, the measured peak displacements were significantly different (200 µm and 66 µm respectively, for a soft and a medium hardness at 177 dB peak) with correlated consequences on the peak noise reduction of the HPD (19 dB peak and 29 dB peak respectively, for a soft and a medium hardness at 177 dB peak). These results suggest new perspectives for HPD evaluation through the choice of judicious ATF materials that conform to biological tissues.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".