Meta-earplugs: Innovative concepts for alleviating the occlusion effect
Bibliographic record
Abstract
Passive earplugs are commonly used to reduce workers' exposure to excessively high noise levels. Yet, they are associated with various discomforts of diverse origins (e.g., acoustic, physical, functional, or psychological). The occlusion effect, characterized by an increased perception of physiological sounds transmitted through bone conduction to the cochlea, presents a challenge, leading to acoustic discomfort, especially at low frequencies (0.1 to 1 kHz) and for shallow or moderate insertion depths of the earplug. Building upon acoustic meta-material principles, this study presents “meta-earplug” concepts designed to alleviate the occlusion effect. The approach focuses on reducing the input impedance of the earplug medial surface either to the characteristic impedance of air, using broadband perfect absorption, or to the acoustic impedance of the open ear canal, resulting in a zero objective occlusion effect. For these purposes, the proposed meta-earplug concepts are made of Helmholtz resonators arranged in parallel or series. Transfer matrix models are used in an optimization process to refine the geometry of the meta-earplugs. Although meta-earplug concepts involving multiple Helmholtz resonators have been preliminarily assessed using an artificial ear, a meta-earplug featuring a single resonator has undergone testing with human participants. The evaluation encompasses both objective measurements and subjective assessments.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".