An Axisymmetric Finite Element Model to Study the Earplug Contribution to the Bone Conduction Occlusion Effect
Bibliographic record
Abstract
An axisymmetric linear elasto-acoustic finite element (FE) model of an occluded human external ear is proposed to simulate the bone conduction occlusion effect (OE). The model consists of a cylindrical ear canal cavity surrounded by layers of biological tissues (skin, cartilage and bone) in which an earplug is inserted. Geometrical and material properties are taken from the literature. OEs are predicted for foam and silicone earplug FE-models using COMSOL Multiphysics (COMSOL®, Sweden). The FE-model is shown to predict the experimental OE measured in two healthy human reference groups wearing foam or silicone earplugs, satisfactorily. Deviations between model and experiment are of similar magnitudes as for previous electro-acoustical OE models. Comparison of the axisymmetric FE-model with two existing gold standard electro-acoustical OE models showed (i) minor differences for shallow occlusion and (ii) a large underestimation for frequencies < 1 kHz, but a good agreement above 1 kHz, for deep insertion. OE differences between silicone and foam earplug types (similar insertion depths, close to bony meatus) were observed experimentally and confirmed with the FE-model. Power balance computations in the ear canal and the earplug (both FE-models) indicate an insertion depth dependent contribution of the earplug type to the OE magnitude.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".