Benchmarking Self-Supervised Audio Representations for IoT-Enabled Acoustic Beehive Monitoring
Bibliographic record
Abstract
self-supervised learning (SSL) has enabled the development of universal feature extractors that have redefined the performance envelope of computer vision and speech applications. Recent works have started to explore SSL in other domains, including bioacoustics, which could have significant societal benefits. Honeybees (Apis mellifera), for example, are crucial pollinators contributing to one-third of global food production. However, massive colony losses in recent years have raised concerns. Traditional hive monitoring methods rely on intrusive visual inspections by beekeepers, which can further disrupt colony dynamics. As such, Internet of Things (IoT)-based automated monitoring systems have emerged, integrating environmental and bioacoustic sensing to enable real-time, noninvasive hive assessment. In this work, we introduce a comprehensive evaluation and benchmarking of general-purpose and bioacoustic audio representations that generalize across various tasks in IoT-enabled acoustic beehive monitoring, even with limited labeled data. Herein, fourteen models are evaluated across four critical tasks: beehive state detection, beehive strength assessment, buzzing identification, and beekeeper voice activity detection. Reported results demonstrate the strong generalizability of existing representations, paving the way for advanced, scalable honeybee colony monitoring and preservation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".