Automated categorization of bioacoustic signals: Avoiding perceptual pitfalls
Why this work is in the frame
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Bibliographic record
Abstract
Dividing the acoustic repertoires of animals into biologically relevant categories presents a widespread problem in the study of animal sound communication, essential to any comparison of repertoires between contexts, individuals, populations, or species. Automated procedures allow rapid, repeatable, and objective categorization, but often perform poorly at detecting biologically meaningful sound classes. Arguably this is because many automated methods fail to address the nonlinearities of animal sound perception. We present a new method of categorization that incorporates dynamic time-warping and an adaptive resonance theory (ART) neural network. This method was tested on 104 randomly chosen whistle contours from four captive bottlenose dolphins (Tursiops truncatus), as well as 50 frequency contours extracted from calls of transient killer whales (Orcinus orca). The dolphin data included known biologically meaningful categories in the form of 42 stereotyped whistles produced when each individual was isolated from its group. The automated procedure correctly grouped all but two stereotyped whistles into separate categories, thus performing as well as human observers. The categorization of killer whale calls largely corresponded to visual and aural categorizations by other researchers. These results suggest that this methodology provides a repeatable and objective means of dividing bioacoustic signals into biologically meaningful categories.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it