Calibration of smartphone-based devices for noise exposure monitoring: Method, implementation, and uncertainties of measurement
Bibliographic record
Abstract
Standardized noise exposure campaigns have as their principle disadvantages the cost of instrumentation and the difficulties associated with practical deployment in the field. Our ongoing research evaluates the suitability of an alternate solution based on smartphone sensing: the occupational noise exposure and its associated measurement uncertainties are estimated from a spatio-temporal analysis of smartphones noise measurements and GPS data. This paper presents a diffuse field calibration method for such smartphone-based devices. The measurements methods and the calculation of expanded uncertainties for a large range of sound levels are detailed. The calibration corrections include a frequency response linearization and an A-weighted sound level correction, which is function of the C-A spectral balance of the sound pressure levels measured. To later ensure a realistic correction, these spectral balance values come from distribution of referenced industrial noise databases. An Android™ “app” has also been developed to measure the noise levels and to compute the calibration and corrections factors. Finally, an laboratory validation is conducted to evaluate, on a population of calibrated smartphone-based devices, the measurement errors associated with such devices as a function of microphone directivity, linearity, and frequency response.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".