Evaluation of Feature Extraction Methods for Bee Audio Classification

Abstract

In recent years, machine learning (ML) methods have been widely used as a powerful tool for monitoring beehives based on bee sound data. In the use of ML algorithms, it is necessary to extract important features from the raw audio. In this study, we investigate the performance of many ML algorithms using five different feature extraction methods. We also compare the results of our experiment with the results from a previous study in the literature. The obtained results show that by choosing the right method of extracting important features, the performance of the ML methods on classifying bee sounds can be improved significantly.