An end-to-end approach for true detection of low frequency marine mammal vocalizations
Bibliographic record
Abstract
Research into automated systems for detecting marine mammal vocalizations within acoustic recordings is expanding internationally due to the necessity to analyze large collections of data collected for passive acoustic monitoring. Recent work towards the development of such systems using Convolutional Neural Networks (CNNs) shows great promise and these systems are capable of generalizing to additional species without having to re-train the entire network [1]. However, to the best of our knowledge, the current deep learning implementations do not perform what we refer to as true detection. Instead these systems are simply capable of determining the presence or absence of a vocalization within a spectrogram. In this work we present a CNN trained on spectrograms containing labelled bounding boxes around low-frequency vocalizations produced by several species of marine mammals. In this way, the CNN can precisely detect vocalizations in terms of both time and frequency, while maintaining the advantage of being generalizable to additional species. [1] M. Thomas, B. Martin, K. Kowarski, B. Gaudet, and S. Stan, "Marine mammal species classification using convolutional neural networks and a novel acoustic representation," in ECML PKDD 2019 (Springer, Cham, 2019).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".