Detection and characterization of marine mammal calls by parametric modelling
Bibliographic record
Abstract
In this paper, we describe a parametric modeling method for detection and characterization o f tonal signals and its application to marine mammal calls.The method tracks dominant frequencies with an adaptive notch filter (ANF), and couples this to a novel, simultaneous detection step.The detection statistic is derived from a measure o f tracking reliability, obtained as a by-product o f the tracking algorithm.Detection therefore comes at little extra computational cost from an algorithm that is fast, simple, and capable of dealing with multiple signals in low signal-to-noise ratios.Frequency estimates are derived directly from the time domain waveform, avoiding the resolution trade-off and other short-comings o f the commonly used spectrogram.The performance of the algorithm is demonstrated on both simulated signals and recordings o f right whale calls.The method is found to be noise robust and capable o f extracting right whale and other calls with a low false alarm rate. s o m m a i r eLe présent article décrit une méthode de modélisation paramétrique pour la détection et la caractérisation des vocalisations tonales de mammifères marins.La méthode consiste à poursuivre les fréquences dominantes à l'aide d'un filtre à encoche adaptatif (ANF), couplé à une étape de détection simultanée innovatrice.La statistique de détection est dérivée d 'une mesure de la fiabilité de poursuite, un sousproduit de l 'algorithme de poursuite.La détection entraîne un coût computationnel additionnel minime, et est à la fois rapide, simple, et capable de traiter des signaux multiples et à de bas rapports signal sur bruit.Les estimations de fréquence sont dérivées directement du domaine temporel et fréquentiel, évitant ainsi les compromis de résolution de la technique spectrogramme utilisée fréquemment.Dans une application sur un fichier d 'une durée de 18-min de l'ensemble de donnée de l'atelier, il est possible de détecter quatre vocalisations probables de baleines franches.83 -Vol.32 No. 2 (
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.000 |
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".