Robustness of perceptual features used for passive acoustic classification of cetaceans to the ocean environment
Bibliographic record
Abstract
Passive acoustic monitoring (PAM) is used to study cetaceans in their habitats, which cover diverse underwater environments. It is well known that properties of the ocean environment can be markedly different between regions, which can result in distinct propagation characteristics. These can in turn lead to differences in the time-frequency characteristics of a recorded signal and may impact the accuracy of PAM systems. To develop an automatic PAM system capable of operating under numerous environmental conditions, one must account for the impact of propagation conditions. A prototype aural classifier developed at Defence R&D Canada has successfully been used for inter-species discrimination of cetaceans. The aural classifier achieves accurate results by using perceptual signal features that model the features employed by the human auditory system. The current work uses a combination of at-sea experiments and pulse propagation modeling to examine the robustness of the perceptual features with respect to propagation effects. Preliminary results will be presented from bowhead and humpback vocalizations that were transmitted over 1–20 km ranges during a two-day sea trial in the Gulf of Mexico. Insight gained from experimental results will be augmented with model results. [Work supported by the U.S. Office of Naval Research.]
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| 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".