Objectivization of the occlusion effect induced by earplugs in laboratory conditions—Effect of earplug type, insertion depth and background noise levels
Bibliographic record
Abstract
Blocking the earcanal’s entrance with an earplug can lead users to experience discomforts, one being the occlusion effect (OE), typically described as a distorted perception of one’s own voice. This discomfort sometimes causes users to misuse or remove their earplugs which significantly lower their efficiency. Reducing the OE generated by earplugs is therefore critical to make them more comfortable. However, assessing the OE is cumbersome and time-consuming as participants’ feedback is required. Moreover, the influence of factors on the OE, namely, the type or earplug, the insertion depth, and the background noise level, remains to be understood. Hence, this ongoing research aims at objectivizing the OE induced by earplugs during speech. To do so, the OE is assessed in laboratory conditions with 30 normal hearing participants using a questionnaire and by using surrogate earplugs for in-ear microphonic measurements. Various sound pressure level-based indicators are proposed and correlated to the (dis)comfort during the objectivization step. Multiple combinations of earplugs, insertion depths and background noise levels are tested to obtain a ranking of the conditions generating more or less OE. The objectivization of the OE will be a useful tool for manufacturers developing new earplugs without requiring participants’ feedback.
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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 | 0.005 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".