Octave band impulse peak insertion loss: a method to characterize hearing protection devices when firing with small arms
Bibliographic record
Abstract
Military Operators (MOs) are exposed to a broad range of impulse noise that can vary greatly in terms of level, temporal and spectral characteristics. The accurate characterization of the performance of hearing protectors is required, to mitigate the hearing damage risk for the MOs working under such conditions. Presently, the accepted method to characterize Hearing Protection Devices (HPDs) performance is based on the measurement of the Impulse Peak Insertion Loss (IPIL); however, this measurement doesn't provide the peak Insertion Loss (IL) per Octave Band (OB). A method for measuring the peak IL per OB for HPDs was developed and is presented in this paper. The concept of an Octave Band Impulse Peak Insertion Loss (OBIPIL) is introduced to account for the HPD peak attenuation at each OB. Using a modified version of the ANSI/ASA S12.42-2010 test setup, the impulse noise signals for a 5.56 mm caliber weapon were recorded. The IPIL and the OBIPIL values of the tested HPDs were computed and are presented for comparison. Moreover, an OBIPIL comparison versus the Bone Conduction (BC) attenuation limits is provided to gauge the attenuation capabilities of each HPD at each OB. A performance behavior for each HPD is presented in the time domain and per OB. Finally, remarks, conclusions, pros and cons of the proposed methodology as well as future work are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".