Designing an acoustical test fixture to evaluate the objective occlusion effect
Bibliographic record
Abstract
Earplugs are commonly used to prevent noise-induced hearing loss. However, their effectiveness is often hindered by the discomfort they cause, impacting consistent and correct use. An important acoustical discomfort, known as the occlusion effect, arises from an increased perception of bone-conducted physiological sounds (such as one's own voice, breathing, and chewing) when the ear canal is occluded. To objectively assess this discomfort, the study proposes the use of an acoustical test fixture (ATF) that avoids direct measurements on human participants. The ATF employs an anatomically realistic truncated outer ear, incorporating soft tissues, cartilage, and bone components to replicate the outer ear's bone conduction path, crucial for occlusion effect assessments. The study demonstrates that the proposed ATF can replicate key effects observed in objective ccclusion effect (OE) measurements on human participants, including significant OE at low frequencies diminishing with increasing frequency, reduction of OE with greater insertion depths, and distinctions among various earplug types—especially noticeable at deeper insertions. Furthermore, a computationally efficient finite element method-based virtual tester for the ATF is developed and validated.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".