Automated classification of avian vocal activity using acoustic indices in regional and heterogeneous datasets
Bibliographic record
Abstract
Abstract Acoustic indices combined with clustering and classification approaches have been increasingly used to automate identification of the presence of vocalizing taxa or acoustic events of interest. While most studies using this approach standardize data collection and study design parameters at the project or study level, recent trends in ecological research are to investigate patterns at regional or continental scales. Large‐scale studies often require collaboration between research groups and integration of data from multiple sources to fulfil objectives, which can lead to variation in recording equipment and data collection protocols. Our objectives were to determine how analytical approaches and variation in data collection and processing that is typical of regional acoustic monitoring programmes influences accuracy when identifying vocal activity in breeding birds. We used data from three regional datasets in Northern Alberta, Northern British Columbia, and Southern and Central Yukon, Canada to investigate the effect of analytical framework, sample size, local species richness and data collection variables on classification accuracy. We found supervised classification approaches to be the most effective, with boosted regression trees identifying vocalizations of breeding birds in audio recordings with a 92.0% accuracy and easily able to accommodate variation in data collection and processing parameters. We also provide recommendations on effectively processing large and heterogeneous datasets including sufficient sample size, accommodating potentially confounding variables and selecting suitable model training data. The results presented in this study can help inform decisions in data collection, data processing, and study design and analysis, maximize performance and accuracy during analysis, and efficiently process large, heterogeneous acoustic datasets to answer questions at scales previously difficult to investigate.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".