Classification of marine mammal vocalizations using an automatic aural classifier.
Bibliographic record
Abstract
Passive sonar systems are often used to detect marine mammal vocalizations in order to localize and track them. Unfortunately, transients generated by sources other than marine mammals can also trigger passive sonar systems which leads to a large number of false alarms. Furthermore, even in the case of a successful detection, classifying the genus is often required and this typically requires expertise not readily available on the vessel. Perceptual signal features—similar to those employed in the human auditory system—have been used to reduce false alarms in active sonar by automatically discriminating between target and clutter echoes. This contributes to improved sonar performance [Young and Hines, J. Acoust. Soc. Am. 122, 1502–1517 (2007)]. Many of the features were inspired by research directed at discriminating the timbre of different musical instruments (a passive classification problem) which suggests it might be applied to classify marine mammal vocalizations. To test this hypothesis, the automatic aural classifier was trained and tested on a set of marine mammal vocalizations from a variety of species. This paper will provide an overview of the aural classifier’s architecture, describe the preparation of the data set, including the attempt to provide ground-truth confirmation of the data, and present some preliminary results.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".