Human and machine annotation in the Orchive, a large scale bioacoustic archive
Bibliographic record
Abstract
Advances in computer technology have enabled the collection, digitization, and automated processing of huge archives of bioacoustic sound. Many of the tools previously used in bioacoustics research work well with small to medium-sized audio collections, but are challenged when processing large collections ranging from tens of terabytes to petabyte size. The Orchive is a system that assists researchers to listen to, view, annotate and run advanced audio feature extraction and machine learning algorithms on large bioacoustic archives. Annotation is one of the biggest challenges in our work. In this paper, we describe our efforts to utilize experts as well as citizen scientists to participate in the process of annotating recordings. The Orchive contains over 23,000 hours of orca vocalizations collected over the course of 30 years, and represents one of the largest continuous collections of bioacoustic recordings in the world. Manual annotation is practically impossible and therefore we investigate the effectiveness of a semi-automatic approach for extracting information from these recordings, and show various experimental results. Finally we have been able to apply our automatic analysis over the a large portion of the archive and describe the computational resources required. To the best of our knowledge this is the largest archive of bioacoustic data that has even been automatically analyzed.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".