Whale cocktail party: Real-time multiple tracking and signal analyses
Bibliographic record
Abstract
This paper provides a real-time passive acoustic method to track multiple vocalizing whales using four or more omni-directional widely-spaced bottom-mounted hydrophones.Since the interest in marine mammals has increased, robust and real-time systems are required.To meet these demands, a real-time tracking algorithm was developed.After non-parametric Teager-Kaiser-Mallat signal filtering, rough Time Delays Of Arrival are calculated, selected and filtered, and used to estimate the positions of whales for a constant, linear or estimated sound speed profile.The complete algorithm is tested on real data from NUWC1 and AUTEC2.Our model is validated by similar results from the US Navy3 and SOEST4 University o f Hawaii Laboratory in the case o f one whale, and by similar results from the Columbia University ROSA5 Laboratory for the case of multiple whales.At this time, our tracking method is the only one which provides typical speed and depth estimates for multiple vocalizing whales. r s u m Ce papier propose une mthode temps-rel de trajectographie par acoustique passive de plusieurs ctacs mettant simultanment en utilisant un rseau d 'au moins 4 hydrophones espacs de quelques centaines de mtres.Etant donn l 'intrt accru pour les mammifres marins, des systmes temps-rel et robustes sont ncessaires.Pour rpondre cette demande, un algorithme temps-rel de trajectographie multiple a t dvelopp.Aprs un filtrage non paramtrique Teager-Kaiser-Mallat du signal, les diffrences de temps d 'arrive aux hydrophones sont estimes, slectionnes, filtres, et permettent d 'estimer les positions des baleines pour un profil de clrit constant, linaire ou estim.L 'algorithme est test sur des donnes relles du NUWC1 et de l 'AUTEC2.Notre modle est valid par des rsultats similaires de l 'US Navy3 et du laboratoire SOEST4 de l'universit d 'Hawaii dans le cas d 'missions simples, et par une estimation du nombre de baleines du laboratoire ROSA5 de l 'universit de Columbia dans le cas de plusieurs missions simultanes.Actuellement, notre mthode de trajectographie est la seule donnant, dans le cas de plusieurs baleines, des vitesses et des profondeurs vraisemblables.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".