BeeVe: Unsupervised Acoustic State Discovery in Honey Bee Buzzing
Hamze Hammami, Nidhal Abdulaziz

TL;DR
This paper presents BeeVe, an unsupervised framework that discovers meaningful acoustic states in honey bee buzzing without labels, enabling non-invasive hive health monitoring through learned audio tokens.
Contribution
BeeVe introduces a novel unsupervised method combining a frozen spectrogram transformer and VQ-VAE to identify stable acoustic states in bee buzzes without supervision.
Findings
Tokens distinguish queenright and queenless hive conditions effectively.
Unsupervised tokens reveal stable sub-states within queenless conditions.
Sequence analysis shows non-random, structured transitions in bee buzzing patterns.
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…
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