A precision sound pressure level measurement system
Bibliographic record
Abstract
Noise pollution affects human and wildlife. It is in the public interest to reduce noise levels. Measures to be taken to reduce the noise are normally expensive and must be based on facts and reliable measurements of noise. Therefore, sound pressure level measurements are key evidence to determine whether there is a breach of noise limits. The sound pressure measurement method should comply with the metrological principles concerning validation, measurement traceability and estimation of measurement uncertainty as stated in ISO/IEC Directives, Part 2, Principles and rules for the structure and drafting of ISO and IEC document. For this reason, a precision sound pressure measurement system has been developed at the National Research Council Canada. The system utilizes the latest advances in digitization and is metrologically transparent, facilitating the evaluation of measurement uncertainties. In this paper, the design and implementation of the system based on IEC 61672-1, Electroacoustics - Sound level meters - Part 1: Specifications is described. The technical challenges, such as truncation, are discussed. Closed-form solutions are obtained for typical sinusoidal sound signals. Experiments were conducted for the validation of the system. The measurement results are presented showing that the discrepancy between theoretical calculations and measurements is only 1.6 ppm.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".