Make the CPUs do the hard work - Automated acoustic feature extraction and visualization for marine ecoacoustics applications illustrated using marine mammal Passive Acoustic Monitoring datasets
Bibliographic record
Abstract
1. Passive Acoustic Monitoring is emerging as a solution for monitoring species and environmental change over large spatial and temporal scales. However, drawing rigorous conclusions based on acoustic recordings is challenging, as there is no consensus over which approaches and indices are best suited for characterizing marine acoustic environments. 2. We present an alternative to the use of ecoacoustic indices and describe the application of multiple machine learning techniques to the analysis of a large PAM dataset. We combine pre-trained acoustic classification models, dimensionality reduction, and random forest algorithms to demonstrate how machine-learned acoustic features capture different aspects of the marine environment. We processed two PAM databases and conducted 13 trials showing how acoustic features can be used to: i) discriminate between the vocalizations of marine mammals, beginning with high-level taxonomic groups, and extending to detecting differences between conspecifics belonging to distinct populations; ii) discriminating amongst different marine environments; and iii) detecting and monitoring anthropogenic and biological sound sources. 3. Acoustic features and their UMAP projections exhibited good performance in the classification of marine mammal vocalizations. Most of the taxonomic levels investigated here could be classified using the UMAP projections, apart from species that were underrepresented. Both anthropogenic (ships and airguns) and biological (humpback whales) sound sources could also be identified in field recordings. 4. We argue that acoustic feature extraction, visualization, and analysis allows the retention of most of the environmental information contained in PAM recordings, overcoming the limitations encountered when using ecoacoustics indices. Acoustic features are universal, permitting comparisons of results collected from multiple environments. Our approach can be used to simultaneously investigate the macro and micro characteristics of marine soundscapes, with a more objective method and with far less human effort.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.049 |
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".