@ARTICLE{Libal_Urszula_MFCC-Based_2024,
 author={Libal, Urszula and Biernacki, Pawel},
 volume={vol. 70},
 number={No 4},
 pages={849–853},
 journal={International Journal of Electronics and Telecommunications},
 howpublished={online},
 year={2024},
 publisher={Polish Academy of Sciences Committee of Electronics and Telecommunications},
 abstract={Abstract—Smart beekeeping is a rapidly developing field. Automated detection and classification of honey bees opens many new opportunities for studies on their behavior. In this paper, we focus on distinguishing between two classes of bees: female workers and male drones. The classification is performed on mel-frequency cepstral coefficients obtained for audio recordings of their flights in a close proximity to an entrance to a beehive. We compare the classification accuracy for several classifiers. We investigate how partitioning of the frequency spectrum influences the classification results. The study involves series of experiments performed for different cepstral representations in the form of 5, 10, 15, 20 and 40 mel-frequency cepstral coefficients.},
 title={MFCC-Based Sound Classification of Honey Bees},
 type={Article},
 URL={http://journals.pan.pl/Content/133209/PDF-MASTER/9_4553_Libal_L_sk.pdf},
 doi={10.24425/ijet.2024.152069},
 keywords={Internet of Things (IoT), smart beekeeping, pattern recognition, signal processing, mel-frequency cepstral coefficients},
}