A 2D axisymmetric finite element model to assess the contribution of in-ear hearing protection devices to the objective occlusion effect
Bibliographic record
Abstract
The use of in-ear devices such as hearing aids or earplugs can be accompanied by an amplification of physiological noises upon their insertion in the earcanal, also called occlusion effect. Classical lumped elements-based prediction models for the occlusion effect consider the source of this effect solely due to the ear canal walls vibration. In most of these models, the in-ear device is considered as a simple infinite acoustic impedance which modifies the ear canal cavity length and thereby the ear canal walls vibrating surface depending on the insertion depth. The contribution of the earplug sound radiation to the occlusion effect is not considered as a significant mechanism. However, some authors have recently demonstrated using a finite element (FE) model that the earplug affect the vibration pattern of the ear canal walls but also may play the role of sound source in the unobstructed ear canal cavity thereby affecting the occlusion effect. In the most sophisticated lumped models, the in-ear device acts additionally as a loading on the ear canal wall which in turn modifies the ear canal free walls vibration. However the skin/in-ear device mechanical coupling is simplified and the earcanal cavity/in-ear device vibroacoustic coupling is ignored. Other physical effects such as leaks between in-ear device and skin may affect the occlusion effect at low frequencies but this has been rarely tackled in the literature. In this paper, a 2D axisymmetric FE model of the human open and occluded external ear including or not the presence of leaks is proposed to calculate the occlusion effect induced by earplugs. This model is used to study the influence on the occlusion effect of the various couplings skin/earplug/earcanal cavity for various insertion depths and leak diameters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 teacher head, 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".