Analyzing Self-Noise Sources and Mitigation Strategies in Glider-Based Passive Acoustic Monitoring
Bibliographic record
Abstract
Passive acoustic monitoring has emerged as an invaluable technique for surveying fisheries, tracking endangered species, and evaluating acoustic propagation models, along with a host of other applications. Gliders (buoyancy-propelled autonomous underwater vehicles) are an attractive platform for passive acoustic monitoring due to their controllability, mission duration, and generally low-noise profile. Hydrophones can be mounted onto gliders externally with self-contained power or integrated into the vehicles for real-time data capability; however, noise generated at inflections (buoyancy pump actuation and battery motor motion) and during navigation (rudder motion and turbulence) can mask critical biological cues while filling the limited data allotment with false positives. Recent glider surveys conducted with an integrated hydrophone off the Georgia/Florida coast during the calving season for the critically endangered North Atlantic right whales suggests that reducing the amount of 'self-noise' could significantly increase the number of whale detections during monitoring missions while also reducing battery consumption and extending the monitoring period. This work characterizes the spectrum three major sources of self-noise, analysis the effectiveness of various flight and mission based noise mitigation strategies, and proposes a preliminary method for removing self-noise from the real-time audio signal. With a focus on both prevention and filtering, this research demonstrates significant improvements in glider-based passive acoustic monitoring, especially in the context of right whale detection. These techniques have the potential for broader application in scientific, industrial, and military settings to improve both the quality and quantity of the acoustic data collected.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".