A neural network for classifying clicks of Blainville's beaked whales (Mesoplodon Densirostris)
Bibliographic record
Abstract
Beaked whales are difficult to detect visually, and researchers have thus proposed using acoustic detection and classification.Because of the large data volumes often involved in acoustic detection and classification, automatic methods are often used.Here a neural network classification method is investigated.Using backpropagation, a feedforward neural network with one hidden layer was trained to classify clicks of Blainville's beaked whales and other odontocetes recorded in the Bahamas.Training and testing data consisted of approximately 1600 Blainville's beaked whale clicks and 3100 clicks from other odontocetes.Networks with 2-10 hidden units were trained and tested, with performance curves (ROC curves) calculated at several levels of signal-to-noise ratio.Results for most networks were quite good when compared with previous classification efforts, with less than 3% errors in both the wrong-classification and missed-call categories.Future work includes testing the network on sounds recorded in different noise backgrounds and from other populations of Blainville's beaked whales, and combining it with a detector and evaluating the joint performance.s o m m a i r e Msoplodons sont difficiles voir et chercheurs ont propos d'employer la dtection et la classification acoustique pour en trouver.Face la quantit de donnes produites par dtection et classification acoustiques, mthodes automatises sont souvent utilises.Ici on present une methode de rseau neuronal pour classifier.Un rseau neuronal rtropropagation non rcurrent avec une seule couche cache a t form pour classifier des clics des Msoplodon de Blainville et autres odontoctes enregistrs aux Bahamas.Les donnes de formation se sont composes d 'environs 1600 clics de Msoplodon de Blainville et 3100 clics d 'autres odonotoctes.Reseaux avec 2-10 units caches ont t forms et examins par courbes caractristiques d'opration du rcepteur (ROC curves) calculs plusieurs niveaux du ratio signal/bruit.Rsultats pour la plupart des rseaux taient tout fait bons en comparaison avec des efforts prcdents de classification avec moins de 3% d 'erreurs chez les clics incorrectement classifis ou manqus.Travaux suivre sont essais du rseau avec les enregistrements venant d 'autres niveaus deu bruit de fond et d 'autres populations de Msoplodon de Blainville, et en combinaison avec un detecteur, une evaluation d 'excution commune.
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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".