Three-Dimensional Auditory Display for Enhancing Detection of Passive Sonar Signals
Bibliographic record
Abstract
OBJECTIVE: The viability of a three-dimensional (3-D) auditory display for improving signal detection of passive sonar signals was investigated. BACKGROUND: Sonar operators usually have difficulty detecting targets because the sound received by the hydrophone has a low signal-to-noise ratio when coupled with the operator's headset that does not isolate well against the ambient noise. METHODS: Release from masking was assessed by pairing a recording of a torpedo with diotic broadband pink noise that served as a masker, and a 400 Hz tone with the masker. Masked thresholds were measured for seven signal durations when each signal was presented dioticly and in 3-D auditory space at three positions on the horizontal plane. RESULTS: The spatial separation of signal and masker yielded a significant improvement in detection. CONCLUSION: A 3-D auditory display is a viable technology that could lead to a significant improvement in release from masking. The magnitude of the masking level difference will vary with respect to the characteristics of the hydrophone signal and masker and the synthesis capability of the 3-D auditory display. APPLICATION: Potential applications of this research include enhanced auditory displays for processing passive sonar signals, leading to earlier detection of enemy targets.
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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 | 0.001 |
| 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 | 0.001 |
| 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".