# Audio-based identification of beehive states

arXiv: https://arxiv.org/abs/1811.06330 · HTML: https://mlsanity.com/paper/1811.06330/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2018.

## Built on by (most cited first, 7 of 7)

- [HEAR: Holistic Evaluation of Audio Representations](https://mlsanity.com/paper/2203.03022/) (2022)
- [BYOL-S: Learning Self-supervised Speech Representations by Bootstrapping](https://mlsanity.com/paper/2206.12038/) (2022)
- [The Efficacy of Self-Supervised Speech Models for Audio Representations](https://mlsanity.com/paper/2209.12900/) (2022)
- [Learning General Audio Representations with Large-Scale Training of Patchout Audio Transformers](https://mlsanity.com/paper/2211.13956/) (2022)
- [Discriminative Singular Spectrum Classifier with Applications on Bioacoustic Signal Recognition](https://mlsanity.com/paper/2103.10166/) (2021)
- [Learning Audio Representations with MLPs](https://mlsanity.com/paper/2203.08490/) (2022)
- [A Survey of TinyML Applications in Beekeeping for Hive Monitoring and Management](https://mlsanity.com/paper/2509.08822/) (2025)

## Abstract (arXiv)

The absence of the queen in a beehive is a very strong indicator of the need for beekeeper intervention. Manually searching for the queen is an arduous recurrent task for beekeepers that disrupts the normal life cycle of the beehive and can be a source of stress for bees. Sound is an indicator for signalling different states of the beehive, including the absence of the queen bee. In this work, we apply machine learning methods to automatically recognise different states in a beehive using audio as input. % The system is built on top of a method for beehive sound recognition in order to detect bee sounds from other external sounds. We investigate both support vector machines and convolutional neural networks for beehive state recognition, using audio data of beehives collected from the NU-Hive project. Results indicate the potential of machine learning methods as well as the challenges of generalizing the system to new hives.