An artificial ear to assess objective indicators related to the acoustical comfort dimension of earplugs: comparison with attenuation and occlusion effect measured on subject
Bibliographic record
Abstract
Hearing protection devices (HPD) are widely used to prevent noise-induced hearing loss (NIHL). In a noisy environment, wearing a correctly fitted HPD during all the time exposure is the sine qua non condition to prevent NIHL. However, this condition is often unfulfilled due to the discomforts in-duced by HPDs. Although this is a well-known fact, it remains challenging to quantify HPD comfort since it is a multidimensional concept related to subjective feelings of the users. Thus, it seems nec-essary to use objective indicators correlated to subjective attributes of HPD comfort to help manufac-turers to design efficient protectors. Those indicators would also help an adequate HPD selection by users. For earplugs, the insertion loss (IL), attenuation and occlusion effect (OE) are good candidates to objectively describe attributes belonging to the acoustical dimension of comfort. However, most of the current ear simulators are not adapted to evaluate the physical variables related to these indi-cators since they do not consider important features of the external ear such as the earcanal geometry or the tissues surrounding it. As part of an ongoing project aiming at developing augmented artificial heads for measuring indicators of the acoustical comfort dimension induced by earplugs, this study presents an artificial ear to assess objective indicators related to attributes of the acoustical comfort dimension of earplugs. This testing device has a realistic geometry and includes synthetic tissues mimicking the real human ear’s bone, cartilage and soft tissues. Its design and fabrication steps are presented. The ability of the device to assess objective indicators related to comfort attributes of ear-plugs is investigated by comparing attenuation and occlusion effect measurements performed on the artificial ear, a subject and an acoustic test fixture. Limits of the device such as its material properties, the absence of tympanic membrane and boundary conditions are 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".