Volume 14, Number 4

Identify the Beehive Sound using Deep Learning


Shah Jafor Sadeek Quaderi1, Sadia Afrin Labonno2, Sadia Mostafa2 and Shamim Akhter3, 1University of Malaya, Malaysia, 2AISIP Lab, International University of Business Agriculture and Technology, Bangladesh, 3Stamford University Bangladesh, Bangladesh


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 Naïve Bayes, with the deep learning techniques. The techniques are also verified on the combined recorded sounds (25-75% noises).


Beehive sound recognition, Audio data feature extraction, Sequential neural network, Recurrent neural network, Convolutional neural network.