Simulation of the objective occlusion effect induced by bone-conducted stimulation using a three-dimensional finite-element model of a human head
Bibliographic record
Abstract
The occlusion effect (OE) refers to the phenomenon that more bone-conducted physiological sounds are transmitted into the earcanal when it is blocked and may cause discomfort on users of hearing protection devices. Models have been proposed to study the OE as they can help understand the physical mechanisms and can be used to evaluate the individual contribution on the OE of the factors that may affect it (i.e., occlusion device, ear anatomy, and stimulation). The existing finite element models developed to study the OE are limited by their truncated ear geometries. In order to progress in the understanding of the OE, the goal of this paper is to develop a finite element model of an entire head to predict the sound pressure field in its earcanals, open or occluded by earplugs. The model is evaluated by comparing the computed input mechanical impedances and OEs in various configurations with literature data. It is able to reproduce common behavior of the OE reported in the literature. In addition, the model is used to assess the effects on the simulated OEs of several parameters, including the modeling of the external air, the boundary condition at the head base and the material properties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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".