DETERMINATION OF THE UNCERTAINTY CONTRIBUTION OF ACOUSTIC FRONT ENDS ON SOUND PRESSURE LEVEL MEASUREMENTS
Bibliographic record
Abstract
Sound pressure level measurements are key points to determine whether there is a breach of noise limits. Any measurement made without the knowledge of its uncertainty lacks signifi- cance. For this reason, an open measurement system has been developed at the National Re- search Council Canada for sound pressure level measurements with the focus on measurement uncertainties. For uncertainty evaluation of such a system, the most difficult task is determining the uncertainty contribution of its acoustic front end, or its microphone and preamplifier assem- bly. The acoustic frond end can be modelled as a linear time-invariant system. Once the fre- quency response of the acoustic front end has been measured, its effect on an arbitrary signal can be analyzed. However, measured microphone frequency response does not include phase in- formation, as no country in the world yet has Calibration and Measurement Capability (CMC) for microphone pressure sensitivity phase. With the completion of key comparison CCAUV.A- K5, this situation will change in the near future. Now is the time to determine the uncertainty contribution of acoustic front end using the phase information available, such as that in CCAUV.A-K5. In this paper, a method for the evaluation of the uncertainty contribution of acoustic front ends is presented. The acoustic front end is first modelled as a linear time- invariant system. Its measured frequency response at discrete frequencies is then curve-fit to ob- tain the response covering the entire frequency domain. The output of the acoustic front end is simply the convolution of the input with the acoustic front end's impulse response. The uncer- tainty contribution is finally calculated by propagating the uncertainties of the acoustic front end output at every time instance. Examples are given for typical acoustic front ends with various types of acoustic signals.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".