Automated tracking of multiple acoustic sources with towed hydrophone arrays
Bibliographic record
Abstract
Line transect surveys often incorporate a towed hydrophone array to detect and localize marine mammals. The animals are typically tracked based on the estimated time difference of arrivals (TDOAs) of their calls between pairs of hydrophones. The estimated TDOAs or bearings are then tracked through time to obtain animal or group positions, a process often performed manually. This process can be especially challenging in the presence of multiple animal groups that are vocalizing simultaneously, but at the same time do not emit signals consistently through time. In addition, the process is hindered by missed detections and false alarms (false TDOAs). Here, an automated approach to TDOA tracking is outlined, based on a multi-target Bayesian framework, that incorporates target appearance, disappearance, missed detections and false alarms. The method is demonstrated on examples of line transect surveys from Western Canada [Norris et al., J. Acoust. Soc. Am.146, 2805 (2019)] and from Hawaii, USA. [In memory of Thomas F. Norris.]
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".