Acoustical corrections to be used for improved in-ear noise dosimetry measurements
Bibliographic record
Abstract
Although much effort has been made to reduce sound at the source, hearing protection devices (HPDs) remain currently the most widely used solution against occupational noise exposure in an attempt to prevent noise-induced hearing loss. Such devices, though, often suffer from uncertainties when it comes to ensuring ideal protection for a given individual. These uncertainties stem mainly from three unknown variables: i) the actual attenuation provided by the HPD to the individual; ii) the ambient noise levels; iii) the wear time of the given HPD. In-ear dosimetry (IED) could overcome these issues, as it makes it possible to establish precise personal noise exposures through real-time in-ear noise monitoring. Furthermore, IEDs can be worn in the open, occluded (under earplugs), or cushioned (under earmuffs) ear. Nevertheless, current IEDs are weakened by two inaccuracies: the non-discrimination of self-induced noise, and the lack of acoustical corrections to convert the measured in-ear sound pressure levels to the eardrum and to the free-field. The latter issue is treated in this presentation, which describes methods to identify the appropriate acoustical corrections to be used for improved IED. Such methods aim at personal calibration procedures, so that the unique ear geometry of each individual is accounted for. Preliminary results will be presented as a comparison between theoretical and experimentally measured corrections on human test-subjects.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".