A simplified axi-symmetric finite element model of the human outer ear to determine the earplug induced auditory occlusion effect
Bibliographic record
Abstract
Earplugs are a frequently used short-term solution for hearing conservation in the workplace environment. Due to limited auditory comfort, however, workers often only wear them for short periods of time and become prone to hearing loss. An important source of discomfort is the auditory occlusion effect, which expresses itself through the distortion of the wearer's voice and the amplification of physiological noises upon earplug insertion. Simplified numerical modeling can help to better assess and design earplugs, because it requires few system resources and is simpler in terms of numerical and experimental implementation than an equivalent complex model. This work describes a novel coupled linear elasto-acoustic two dimensional finite element (FE) model of the human outer ear. The model comprises the auditory canal as well as the bony, cartilaginous, and skin tissues whose material parameters were approximated using literature findings. The outlined model can compute the transfer functions between the sound pressure levels at the eardrum and a structure-borne excitation for both an unoccluded ear and an ear occluded by a molded earplug. Simulated occlusion effects are examined as a function of excitation, earplug, and insertion depth. Predicted model results are compared to literature findings and to findings obtained from an equivalent three dimensional FE-model.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".