Predicting impulse prominence and tone audibility at remote assessment locations
Bibliographic record
Abstract
This paper studies the prediction of impulse prominence and tone audibility at remote assessment locations, with a focus on industrial and commercial sound. The British Standard 4142 provides objective methods for determining graded corrections to the equivalent continuous sound pressure level for impulsivity and tonality, but these methods require specific sound sources to be installed and operating. In planning applications, where the sources are proposed or the propagation path is intended to change significantly, the professional judgement of the acoustics practitioner is relied upon to determine the likely prominence/audibility of these features at a remote assessment location. To address this issue, online listening tests utilising simplified auralisation ('auralisation-lite') were conducted among members of the Association of Noise Consultants in the UK. The initial results suggest that objective evaluation can indeed offer an improvement over existing subjective evaluations. The work provides a new method for practitioners to predict tonal and impulsivity corrections for non-existent sources. This study also provides a comprehensive review of relevant literature and standards, including ISO 9613-2:1996 and ISO/TR 17534-3:2015. The findings have practical implications for environmental agencies, industrial settings, and other fields where accurate prediction of sound level impacts are crucial.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".