BeeVe: Unsupervised Acoustic State Discovery in Honey Bee Buzzing

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 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.