Bibliographic record
Abstract
In this paper we present a novel approach for the automatic detection of clicks from recordings of beaked whales based on the phase characteristics of minimum phase signals and especially using the group delay function.Group delay is estimated through the and first derivative of the Fourier Transform of a signal.A major advantage of the proposed approach is its robustness against additive noise while it doesn't require the definition of ad-hoc or adaptive thresholds for the detection of clicks.This method works on raw recordings which are usually quite noisy as well as on click enhanced recordings (after band-pass filtering or using operators like the Teager-Kaiser energy operator).Moreover, a click is just detected by searching the positive zero crossings over time of the slope of the phase spectrum.To evaluate the effectiveness of the proposed approach in detecting clicks, a oneminute recording has been manually marked providing a test set of about 320 clicks.Results show that the proposed approach was able to detect 71.37% of the hand labelled clicks within an accuracy of 3 ms. SOMMAIREDans cet article, nous prsentons une nouvelle approche pour la dtection automatique de clics sur des enregistrements de baleines bec exploitant les caractristiques de signaux phase minimale notamment via l' utilisation de la fonction de retard de groupe.Le retard de groupe est estim partir de la transforme de Fourier d'un signal et de la drive de celle-ci.L'approche propose est robuste vis--vis du bruit additif et ne requiert pas la dfinition ad-hoc ou adaptative de seuils pour la dtection de clics.Elle permet de traiter aussi bien des enregistrements bruts fortement bruits que des enregistrements rehausss (aprs filtrage passe-bande ou l'aide d'oprateurs tels que l'oprateur d'nergie de Teager-Kaiser).De plus, un clic est simplement dtect en recherchant un passage par zro sur la partie croissante de la pente du spectre de phase.Pour valuer l'efficacit de l'approche propose dtecter des clics, une minute d'enregistrement a t annote manuellement, fournissant ainsi un ensemble de test d'environ 320 clics.Les rsultats montrent que l'approche propose parvient dtecter 71.37% des clics marqus manuellement avec une prcision de 3 ms.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".