On the acceptance limits for short-term level fluctuation of sound calibrators
Bibliographic record
Abstract
To ensure noise measurement accuracy, sound calibrators are used to check the sound level meter accuracy before and after any such measurements, as required by most noise regulations. The sound calibrator itself needs to be calibrated routinely and traceable to national standards. The International Standard IEC 60942, Electroacoustics - Sound calibrators, specifies performance requirements for the sound pressure level, short-term level fluctuation, frequency, and distortion and associated test methods. However, the acceptance limits for short-term level fluctuation specified in IEC 60942 is problematic. The short-term level fluctuation acceptance limits specified in Table 2 of IEC 60942 for the level variations inherited by a stable sinusoid at low frequencies are either underestimated or overestimated. In this paper, a detailed theoretical analysis is presented addressing this particular issue. Closed-form solutions are obtained for typical sinusoidal sound signals. Two experimental setups and measurement results for the validation of the theoretical analysis are also presented. One uses a commercial sound level meter and the other uses a precision sound pressure measurement system developed recently at the National Research Council Canada. A revised table for the acceptance limits is then proposed for the future revision of Table 2 of IEC 60942.
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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 | 0.019 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".