Cross-comparison of the optical and acoustical calibration methods for microphones based on microelectromechanical system technologies
Bibliographic record
Abstract
Calibration methods and associated international standards in airborne acoustics have enabled national metrology institutes to establish and maintain a fully defined traceability chain for designated laboratories and end users of microphones. However, such microphones need to be reciprocal, of condenser type and of specific dimensions; this, in effect, does not allow for the calibration of alternative existing sensor technologies and creates a further obstacle for the calibration of emerging technologies. An example relates to microphones based on microelectromechanical systems which are an integral part of a wide range of electronic devices; yet, there is no established calibration traceability chain other than methods employed by manufacturers. From a metrological perspective, new calibration methods that can accommodate microphones regardless of their size, reciprocal nature and utilised technology must be developed and subsequently standardised and adopted. This paper discusses the optical calibration method based on free field photon correlation and the acoustical calibration methods based on pressure comparison and free field substitution. These approaches can provide traceability to the international measurement standard system for novel types of microphones, and the measurement results showed themselves to be consistent. Based on this study, the possibility of expanding the new standard system was investigated.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".