Localization of Impulsive Sound Source in ShallowWaters using a Selective Modal Analysis Algorithm
Bibliographic record
Abstract
Passive remote monitoring applications of underwater signal processing in a shallow water environment are an impactful area of research for environmental and marine-life monitoring. The majority of the sound source localization techniques require carefully placed synchronized hydrophone arrays, which can be complicated and hard to maintain. In this paper, we utilized the modal dispersions of a signal to derive a localization method for a noisy, shallow water environment. Our proposed algorithm employs modal selection to process the most noise- resistive dispersion curves, improving the accuracy and noise-resistivity of the existing methods. Moreover, we proposed a 2D localization method with multiple unsynchronized hydrophones and minimal hardware requirements and limitations. Furthermore, we analyzed the effects of underwater ambient noise on the accuracy of the proposed method, using simulated and real recorded explosion and whale sounds, and compared our algorithm’s localization performance with others. Simulation results show increased localization accuracy of 30m for the recorded explosion sound and 360m for the Whale sound.
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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 | 0.001 | 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) | 0.001 | 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 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".