A Psychoacoustical Model for Specifying the Level and Spectrum of Acoustic Warning Signals in the Workplace
Bibliographic record
Abstract
A psychoacoustic model is presented to facilitate the installation of acoustic warning devices in noisy settings, reflecting a major upgrade of a former tool, Detectsound. The model can be used to estimate the optimal level and spectrum of acoustic warning signals based on the noise field in the workplace, the hearing status of workers, and the attenuation provided by hearing protectors. The new version can be applied to a wider range of situations. Analyses can now be conducted to meet the functional requirements for a specific worker or to suit the needs for a group of co-workers sharing a work area. Computation of optimal warning signals can also be made from estimated hearing parameters based on the worker age, gender, and level and duration of noise exposure. The results of a laboratory validation study showed that the mean error in estimating detection thresholds for normal hearing individuals is typically within +/-1 dB with a standard deviation of less than 2.5 dB in white noise or continuous noise fields. The model tends to yield slightly overestimated warning signal detection thresholds in fluctuating noises. Proper application of the tool also requires consideration of the variability in estimating noise levels, hearing status, and hearing protector attenuation under field conditions to ensure that acoustic warning signals are sufficiently loud and well adjusted in practice.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".