On the design of an acoustical test fixture for assessing the objective occlusion effect
Bibliographic record
Abstract
• An anatomically realistic truncated ear is proposed for quantifying the occlusion effect. • The artificial ear can reproduce key effects observed in human participants. • It can be used to design hearing protectors, hearing aids or earbuds with a reduced occlusion effect. Earplugs are widely used to prevent noise induced hearing loss. However, the discomforts they induce negatively impact their effectiveness by influencing their consistent and correct use. The occlusion effect discomfort is related to an increased perception of the bone-conducted part of physiological sounds (e.g., one’s own voice, breathing and chewing) when one’s earcanal is occluded. The discomfort experienced could be objectively estimated by calculating the objective occlusion effect, which is the difference between the tympanic sound pressure levels in the occluded and open earcanals when exposed to the same stimulation of a bone transducer. To avoid direct measurements on human participants, this work proposes an acoustical test fixture (ATF) for quantifying the objective occlusion effect. The proposed ATF employs an anatomically realistic truncated ear, incorporating soft tissues, cartilage, and bone components to replicate the outer ear bone conduction path crucial for occlusion effect assessments. It is shown that the proposed ATF can reproduce key effects observed in objective OE measurements on human participants: (i) significant OE at low frequencies, diminishing with increasing frequency, (ii) reduction of OE with greater insertion depths, and (iii) distinctions among various earplug types, particularly noticeable at deeper insertions compared to shallow ones. The proposed ATF can therefore be used to design in-ear devices with a reduced occlusion effect, leading to an improved experience for many users of hearing protectors, hearing aids, and earbuds. Additionally, a computationally efficient Finite Element Method-based virtual tester for the ATF is developed and validated. This virtual tester is employed to deepen the comprehension of the physical phenomena that underlie the observed vibroacoustic behavior of the proposed ATF. It also opens avenues for future research aimed at re-evaluating ATF design parameters and enhancing OE assessment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".