Identify The Beehive Sound Using Deep Learning

TL;DR

This paper explores using deep learning models like CNN, RNN, and SNN to classify beehive sounds from recordings, comparing their effectiveness with traditional machine learning methods to aid in monitoring bee populations.

Contribution

It introduces a deep learning-based approach for beehive sound classification and compares its performance with traditional classifiers, including tests on noisy recordings.

Findings

Deep learning models outperform traditional methods in classifying beehive sounds.

CNN and RNN show high accuracy in noisy environments.

Traditional classifiers perform less effectively than deep learning models.

Abstract

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).