Use of passive hearing protectors and adaptive noise reduction for field recording of otoacoustic emissions in industrial noise
Bibliographic record
Abstract
Distortion Product Otoacoustic Emissions (DPOAEs) can detect noise-induced hearing loss in-field, but their data extraction is very sensitive to background noise. This paper investigates how passive and active noise reduction enhance DPOAE recording based on data collected in white noise from 54dB(A) to 90dB(A). Despite considerable high-frequency attenuation from a proper placed DPOAE probe, 54dB(A) background noise deteriorates the test outcome substantially. More low-frequency attenuation by an extra passive earmuff enables measurements in white noise levels of 70dB(A). The relationship between external sound level and noise recorded by the DPOAE system has been statistically modeled. Additionally, the upper limits of attenuation improvement are analyzed by quantifying residual physiological noise. Furthermore, for an earplug integrating microphone and speakers of the DPOAE measurement probe, adaptive noise reduction processing on the DPOAE signal is used to improve the Signal-to-Noise ratio. The adaptive noise reduction (ANR) is implemented using the NLMS algorithm to filter out the ambiant noise, measured by the first microphone measuring the DPOAE signal, with a second miniature microphone mounted flush with the external faceplate of the isolating DPOAE probe. Simulated data shows that DPOAE response extraction is possible in an environment with noise levels exceeding 70dB(A).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".