BeeVe: Unsupervised Acoustic State Discovery in Honey Bee Buzzing
Abstract
Discovering structure in biological signals without supervision is a fundamental problem in computational intelligence, yet existing bioacoustic methods assume vocal production models or predefined semantic units, leaving non-vocal species poorly served. This work introduces BeeVe, an unsupervised framework for acoustic state discovery in collective honey bee buzzing. BeeVe uses the self-supervised Patchout Spectrogram Transformer (PaSST) as a frozen feature extractor, then trains a Vector-Quantized Variational Autoencoder (VQ-VAE) without labels on those embeddings, learning a finite discrete codebook of acoustic tokens directly from unlabelled hive audio. No labels, pretext tasks, or contrastive objectives are used at any stage. Post-hoc evaluation against known queen status reveals that the learned tokens separate queenright and queenless conditions with Jensen-Shannon Divergence values between 0.609 and 0.688, and that the queenless condition further decomposes into three internally coherent sub-states stable across experiments with different codebook sizes and random seeds. Token transition analysis confirms non-random sequential structure (p << 0.001) across all experiments. Generalisation to unseen recordings preserves both token overlap (Jaccard = 0.947) and global manifold topology. These results demonstrate that unsupervised discrete codebook learning can recover repeatable acoustic structure from a non-vocal biological signal without annotation, opening a path toward non-invasive acoustic hive health monitoring.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a significant advancement in unsupervised learning for bioacoustic state discovery, demonstrating the ability to extract structured acoustic patterns from honey bee buzzing without prior assumptions or annotations. The methodology is innovative and the results impactful, contributing to both machine learning and ecological monitoring fields.
Methodology
The paper introduces BeeVe, a novel unsupervised framework for acoustic state discovery in honey bee buzzing, leveraging a self-supervised Patchout Spectrogram Transformer (PaSST) as a feature extractor and a Vector-Quantized Variational Autoencoder (VQ-VAE) for learning a discrete codebook of acoustic tokens. The methodology is well-structured, employing a rigorous unsupervised learning approach without relying on predefined labels or semantic assumptions. The use of post-hoc evaluation against known queen status to validate the learned tokens adds robustness to the methodology. However, the choice of PaSST as a frozen feature extractor, while justified, may limit the model's adaptability to other non-vocal species.
Results & evaluation
The experiments are comprehensive, utilizing a dataset of honey bee audio to assess the effectiveness of the proposed method. The results demonstrate significant separation between queenright and queenless conditions, with Jensen-Shannon Divergence values indicating meaningful distinctions. The identification of stable sub-states within the queenless condition and the analysis of token transition patterns provide strong evidence of the model's capability to uncover structured acoustic states. The metrics used for evaluation, including Jaccard overlap and manifold projection, are appropriate and effectively illustrate the model's performance.
Reproducibility
The paper provides detailed implementation details, including the architecture of the VQ-VAE, training objectives, and evaluation metrics, which contribute to reproducibility. However, the absence of a publicly accessible code repository or demo limits the ability for other researchers to replicate the findings directly.
Limitations
The study is limited by its reliance on a controlled subset of the UrBAN dataset, which may not fully capture the diversity of acoustic states across different hives and conditions. Additionally, while the findings are promising, the lack of biological annotation to ground the discovered states raises questions about their true biological relevance. The scalability of the approach to larger datasets and more complex hive conditions remains to be validated.
Broader impact
The implications of this work extend to non-invasive monitoring of honey bee colonies, potentially aiding in the early detection of conditions such as queen loss or swarming. The unsupervised nature of the framework allows for the identification of previously unlabelled states, which could enhance hive management practices and contribute to pollinator conservation efforts. The approach also opens avenues for future research in bioacoustics and machine learning applications in non-vocal species. The paper presents a significant advancement in unsupervised learning for bioacoustic state discovery, demonstrating the ability to extract structured acoustic patterns from honey bee buzzing without prior assumptions or annotations. The methodology is innovative and the results impactful, contributing to both machine learning and ecological monitoring fields.
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