Unsupervised VQ-VAE training on PaSST embeddings discovers repeatable discrete acoustic tokens in honey bee buzzing that separate queenright from queenless conditions and identify three stable sub-states in queenless hives.
arXiv preprint arXiv:2411.07186 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.SD 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
The paper introduces CoarseSoundNet, a deep learning model for classifying biophony, geophony, and anthropophony in passive acoustic monitoring recordings, reporting performance gains from additional similar data, a silence class, and decision thresholds, plus a case study on acoustic index trends.
citing papers explorer
-
BeeVe: Unsupervised Acoustic State Discovery in Honey Bee Buzzing
Unsupervised VQ-VAE training on PaSST embeddings discovers repeatable discrete acoustic tokens in honey bee buzzing that separate queenright from queenless conditions and identify three stable sub-states in queenless hives.
-
CoarseSoundNet: Building a reliable model for ecological soundscape analysis
The paper introduces CoarseSoundNet, a deep learning model for classifying biophony, geophony, and anthropophony in passive acoustic monitoring recordings, reporting performance gains from additional similar data, a silence class, and decision thresholds, plus a case study on acoustic index trends.