Divided Listening in Noise in a Mock-up of a Military Command Post
Bibliographic record
Abstract
This study investigated divided listening in noise in a mock-up of a vehicular command post. The effects of background noise from the vehicle, unattended speech of coworkers on speech understanding, and a visual cue that directed attention to the message source were examined. Sixteen normal-hearing males participated in sixteen listening conditions, defined by combinations of the absence/presence of vehicle and speech babble noises, availability of a vision cue, and number of channels (2 or 3, diotic or dichotic, and loudspeakers) over which concurrent series of call sign, color, and number phrases were presented. All wore a communications headset with integrated hearing protection. A computer keyboard was used to encode phrases beginning with an assigned call sign. Subjects achieved close to 100% correct phrase identification when presented over the headset (with or without vehicle noise) or over the loudspeakers, without vehicle noise. In contrast, the percentage correct phrase identification was significantly less by 30 to 35% when presented over loudspeakers with vehicle noise. Vehicle noise combined with babble noise decreased the accuracy by an additional 12% for dichotic listening. Vision cues increased phrase identification accuracy by 7% for diotic listening. Outcomes could be explained by the at-ear energy spectra of the speech and noise.
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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 | 0.003 | 0.001 |
| 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) | 0.001 | 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".