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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".