# Identify The Beehive Sound Using Deep Learning

arXiv: https://arxiv.org/abs/2209.01374 · HTML: https://mlsanity.com/paper/2209.01374/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 3 Sep 2022.

## Builds on (most cited first, 2 of 2)

- [A Comparison of deep learning methods for environmental sound](https://mlsanity.com/paper/1703.06902/) (2017)
- [To bee or not to bee: Investigating machine learning approaches for beehive sound recognition](https://mlsanity.com/paper/1811.06016/) (2018)

## Abstract (arXiv)

Flowers play an essential role in removing the duller from the environment. The life cycle of the flowering plants involves pollination, fertilization, flowering, seed-formation, dispersion, and germination. Honeybees pollinate approximately 75% of all flowering plants. Environmental pollution, climate change, natural landscape demolition, and so on, threaten the natural habitats, thus continuously reducing the number of honeybees. As a result, several researchers are attempting to resolve this issue. Applying acoustic classification to recordings of beehive sounds may be a way of detecting changes within them. In this research, we use deep learning techniques, namely Sequential Neural Network, Convolutional Neural Network, and Recurrent Neural Network, on the recorded sounds to classify bee sounds from the nonbeehive noises. In addition, we perform a comparative study among some popular non-deep learning techniques, namely Support Vector Machine, Decision Tree, Random Forest, and Na"ive Bayes, with the deep learning techniques. The techniques are also verified on the combined recorded sounds (25-75% noises).