Ketos—A deep learning package for creating acoustic detectors and classifiers
Bibliographic record
Abstract
Passive acoustic monitoring is a useful technique for studying aquatic animals, but sustained observing systems require automated algorithms for detecting and classifying sounds of interest. In the last decade, deep neural networks have proven highly successful at solving a wide range of pattern recognition tasks, and recently, we have seen the first promising applications of deep neural networks to detection and classification tasks in marine bioacoustics. Deep neural networks exhibit a high degree of versatility and adaptability: the same network architecture can be trained to accomplish a multitude of tasks by feeding appropriate training data to the network without the need to modify the underlying algorithm. Thus, neural networks have the potential to transform our approach to developing acoustic detection and classification programs, enabling researchers in the field to develop or re-purpose their own programs. MERIDIAN is contributing towards this goal through the development of the open-source Python package Ketos, which provides a high-level programming interface for building training datasets and developing neural network based detectors and classifiers for analyzing underwater acoustics data. In this contribution, an overview of the software package will be given and its functionalities will be demonstrated through case studies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.026 |
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".