Towards Adjustable Loudness Compensation in Hearing Protectors for Musicians
Bibliographic record
Abstract
Professional musicians are regularly exposed to excessive sound pressure levels, increasing the risk of noise-induced hearing loss. At present, several discomfort factors (physical comfort, acoustical comfort, etc.) limit the widespread use of hearing protection (HPD) for musicians. For acoustic comfort, two major effects are known to be the most detrimental: the isolation effect, which affects how external sounds are perceived (usually with unbalanced frequency content), and the occlusion effect, which amplifies all internal sounds (such as voice or instrument sound but also swallowing, breathing. etc.) to an uncomfortable level. Two studies on the isolation effect in HPDs are presented. The first study evaluates the improvement of classic electronic loudness compensators to the acoustic comfort of hearing protectors. From the questionnaire filled out by the participants, all from music-related fields, musicians unanimously expressed the desire to adjust their protectors. However, the results of subjective acoustic assessments showed a preference for uncompensated sound. Such preference, which is in disagreement with previous studies on loudness compensators, could be explained by the maximum level of the output that was an A-weighted overall level.The second study investigates the relevance of compensators in a context where an overall level constraint is required. In this experiment, participants adjust the parameters of various electronic loudness compensation filters to match the timbre to the original, non-attenuated, sound extract.Through those two studies, we wish to improve our understanding of musicians' HPD adjustment needs. In the first study, we evaluated the performance of existing loudness compensators in the context of HPDs. In the second, we evaluate different adjustment control schemes and filters to find the balance between adjustment simplicity and compensation effectiveness.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".