Measurement and Characterization of Hearing Protection Devices in the Presence of Impulse Sound
Bibliographic record
Abstract
Impulsive sounds can cause severe hearing damage and even hearing loss. Sound protection devices are widely used to attenuate impulsive sounds and reduce their impact on hearing. Properly measuring and characterizing the sound attenuation is essential when choosing a specific hearing protection device. Currently, hearing protection devices are often characterized using the Impulse Peak Insertion Loss (IPIL) that measures the total attenuation across all frequency bands. IPIL does not provide any information about the spectral attenuation of the device. Human hearing is spectrally sensitive, and the risk of noise-induced hearing damage is frequency-dependent. Therefore, characterizing hearing protection devices has to be done for both the peak and the full audible frequency spectrum from 20 Hz to 20k Hz. In this paper, we propose a novel energy preserving method for estimating the 1/3 octave band insertion loss using the continuous wavelet transform. To do so, we collected gunshot audio sounds from firing a sniper rifle and evaluated the sound attenuation effect of adding a sound protection device (or sound suppressor) to the rifle. The method that we called Wavelets Octave Band Insertion Loss (WOBIL) is compared with existing methods such as the IPIL, the Impulsive Spectral Insertion Loss (ISIL) and the recently published Octave Band Impulse Peak Insertion Loss (OBIPIL).
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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".