Sound localization with an army helmet worn in combination with an in-ear advanced communications system
Bibliographic record
Abstract
Conventional hearing protection devices result in decrements mainly in the ability to distinguish front from rearward sound sources. The aim of this study was to investigate the effect of wearing an earplug with advanced communications capability, in combination with an army helmet, on horizontal plane speaker identification. Ten normal-hearing male subjects were tested in a semi-reverberant sound proof booth under eight conditions defined by combinations of two levels of ear occlusion (unoccluded and occluded by the earplug) and four levels of the helmet (head bare and fitted with the helmet modified to give no, partial and full ear coverage). Percent correct speaker identification was assessed using a horizontal array of eight loudspeakers surrounding the subject at one meter. These were positioned close to the midline and interaural axes of the head, at ear level. The stimulus was a 75-dB SPL, 300-ms broadband white noise. Both degree of ear coverage and ear occlusion significantly determined outcome. Overall percent correct ranged from 93.6% (bareheaded) to 79.7% (full ear coverage) with the ears unoccluded, and from 83.4%-77.5% with ear occlusion. Both variables affected the prevalence of mirror image confusions for positions 30 degrees apart in front and back of the interaural axis. With ear occlusion, front given back errors were more likely than back given front errors, increasing with degree of ear coverage to 49% and 25.4%, respectively. These errors also increased with ear coverage with the ears unoccluded, but were similar. Both degree of ear coverage and ear occlusion significantly impacted horizontal plane speaker identification, particularly for sources close to the interaural axis. However, overall percent correct was higher than observed in a previous study with conventional and level-dependent hearing protection devices, using the same array.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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 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".