Design and laboratory validation of an in-ear noise dosimetry device
Bibliographic record
Abstract
The assessment of noise exposure is a major component of any hearing conservation program. It is \nusually conducted using either sound level meters (SLM) or personal noise dosimeters (PND). Some \nsignificant problems arise when performing noise exposure measurements using SLMs or PNDs. \nFirst, these devices do not usually account precisely for the variations of the actual noise exposure \nexperienced by a given worker over the work shift. Additionally, the attenuation provided by the hearing \nprotector that is worn is only taken into account very approximately. To address these issues, the \npurpose of this study is to implement a precise and reliable in-ear noise dosimetry system based on \nlow computational algorithms that can be used in a wide variety of environments and work conditions. \nThe adopted methodology focuses on the implementation and validation of a real-time measurement \nsystem. The proposed system utilizes a set of two miniature microphones placed inside and outside \nthe earcanal. Besides, two types of in-ear prototypes were developed: one for the unoccluded ear \nand one for the ear occluded with a passive earplug. The validation of the in-ear devices prototypes \nand algorithm implementation was done using data collected during a study on human subjects. Both \ndeveloped hardware and software elements make it possible to determine correction factors enabling \nthe conversion of the measured in-ear noise exposure levels to their equivalent free-field values. Furthermore, \nthe implemented algorithms can detect and exclude wearer-induced disturbances (speech, \nmicrophonics, etc.) making it possible to assess noise exposure with and without the energy contribution \nfrom these self-generated noises. This paper presents a successful implementation and validation \nof an in-ear noise dosimetry system, with unique features and capabilities, that could lead to the \nimprovement of methods and systems for personal noise exposure assessments in the workplace. \nKeywords: acoustics, in-ear noise dosimetry, hearing-protection, occupational health and safety, \nacoustics, instrumentation, real-time algorithms implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".