Bibliographic record
Abstract
Many cetacean vocalisations are tonal and most are frequency-modulated.The detection algorithm presented here breaks the frequency contour into a sequence of elements.Each element is sufficiently short that a linear approximation to the frequency contour can be made.In this way the problem is simplified from that o f detection o f an unknown signal, to the detection o f a known signal (a linear chirp) with unknown parameters.The method o f estimation is based on maximum likelihood, and the start frequency, chirp rate and amplitude o f each element are estimated.Further analysis is then carried out on groups of concatenated chirps (i.e.calls) to classify them.Results are given on performance for the supplied test recording and for synthetic signals in white noise.The pros o f the algorithm are: good detection performance, at least in white noise; high resolution; ease of interpretation; flexibility; data compression.The cons are: computational cost; deterioration o f performance in non-white noise or with amplitude-modulated signals.Further development is needed to reduce errors with overlapping tonal or non-tonal signals.The algorithm is currently being applied to the problem of detecting right whale vocalisations and distinguishing them from those o f humpback whales. r s u m Plusieurs vocalisations de ctacs sont de type tonal et la plupart sont modules en frquence.L 'algorithme de dtection prsent ici spare le contour de frquence en une squence d 'lments.Chacun des lments est suffisamment petit pour qu'une approximation linaire du contour de frquence puisse tre effectue.Le problme est donc en ce sens simplifi de faon ce que la dtection d 'un signal inconnu passe celle d 'un signal connu (une modulation linaire de frquence) avec des paramtres inconnus.La mthode d 'estimation est base sur le maximum de vraisemblance, et la frquence de dpart, le taux de modulation et l'amplitude de chacun des lements sont estims.Des analyses plus pousses sont alors effectues sur des groupes de modulations enchanes (i.e.vocalisations) afin de classifier les sons comme tant du bruit ou comme faisant partis d 'une espce spcifique.Les rsultats sont tirs de la performance des donnes de test et de signaux synthtiques en prsence de bruit blanc.Les avantages de cet algorithme sont: une bonne performance de dtection, du moins l 'intrieur d'un bruit blanc; une haute rsolution; la facilit d 'interprtation; la flexibilit; la compression de donnes.Les dsavantages sont: les cots computationnels; la dtrioration de la performance l 'extrieur d 'un bruit blanc ou avec un signal modul en amplitude.Des dveloppements plus pousss sont requis afin de rduire les erreurs provenant de la superposition d 'un son tonal sur un son non tonal.L 'algorithme a t appliqu aux problmes de dtection des vocalisations des baleines franches ainsi qu' celui de la distinction de leurs vocalisations avec celles des rorquals bosses.
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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) | 0.000 | 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".