Comparison of Feature Extraction Methods for Sound-Based Classification of Honey Bee Activity

Honey bees are one of the most important insects on the planet since they play a key role in the pollination services of both cultivated and spontaneous flora. Recent years have seen an increase in bee mortality which points out the necessity of intensive beehive monitoring in order to better understand this phenomenon and try to help these important insects. In this scenario, this work presents an algorithm for sound-based classification of honey bee activity reporting a preliminary comparison between various extracted features used separately as input to a convolutional neural network classifier. In particular, the orphaned colony situation has been considered using a dataset acquired in a real situation. Different experiments with different setups have been carried out in order to test the performance of the proposed system, and the results have confirmed its potentiality.