Using Auditory Steady-State Responses for Measuring Hearing Protector Attenuation
Bibliographic record
Abstract
INTRODUCTION: Present methods of measuring the attenuation of hearing protection devices (HPDs) have limitations. Objective measurements such as field microphone in real-ear do not assess bone-conducted sound. Psychophysical measurements such as real-ear attenuation at threshold (REAT) are biased due to the low frequency masking effects from test subjects' physiological noise and the variability of measurements based on subjective responses. An auditory steady-state responses (ASSRs) procedure is explored as a technique which might overcome these limitations. SUBJECTS AND METHODS: Pure tone stimuli (500 and 1000 Hz), amplitude modulated at 40 Hz, are presented to 10 normal-hearing adults through headphones at three levels in 10 dB steps. Two conditions were assessed: unoccluded ear canal and occluded ear canal. ASSR amplitude data as a function of the stimulation level are linearized using least-square regressions. The "physiological attenuation" is then calculated as the average difference between the two measurements. The technical feasibility of measuring earplug attenuation is demonstrated for the group average attenuation across subjects. RESULTS: No significant statistical difference is found between the average REAT attenuation and the average ASSR-based attenuation. CONCLUSION: Feasibility is not yet demonstrated for individual subjects since differences between the estimates occurred for some subjects.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".