Towards a practical methodology for assessment of the objective occlusion effect induced by earplugs
Bibliographic record
Abstract
The occlusion effect (OE) occurs when the earcanal becomes occluded by an in-ear device, sometimes leading to discomforts experienced by the users due to the augmented perception of physiological noises, or to a distorted perception of one's own voice. The OE can be assessed objectively by measuring the amplification of the low-frequency sound pressure level (SPL) in the earcanal using in-ear microphones. However, as revealed by methodological discrepancies found in past studies, the measurement of this objective occlusion effect (OEobj) is not standardized. With the goal of proposing a robust yet simple methodology adapted for field assessment, three experimental aspects are investigated: (i) stimulation source and the stimulus's characteristics to induce the phenomenon, (ii) measurement method of the SPL in earcanal, (iii) indicator to quantify the OEobj. To do so, OEobj is measured on human participants in laboratory conditions. Results obtained with a specific insert device suggest using the participant's own voice combined with simultaneous measurements of the SPLs based on the noise reduction method and using a single value indicator leads to a simple yet robust methodology to assess OEobj. Further research is necessary to validate the results with other devices and to generalize the methodology for field assessment.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".