Automated classification of bees and hornet using acoustic analysis of their flight sounds

Satoshi Kawakita , Kotaro Ichikawa

AbstractTo investigate how to accurately identify bee species using their sounds, we conducted acoustic analysis to identify three pollinating bee species (Apis mellifera, Bombus ardens, Tetralonia nipponensis) and a hornet (Vespa simillima xanthoptera) by their flight sounds. Sounds of the insects and their environment (background noises and birdsong) were recorded in the field. The use of fundamental frequency and mel-frequency cepstral coefficients to describe feature values of the sounds, and supported vector machines to classify the sounds, correctly distinguished sound samples from environmental sounds with high recalls and precision (0.96–1.00). At the species level, our approach could classify the insect species with relatively high recalls and precisions (0.7–1.0). The flight sounds of V.s. xanthoptera, in particular, were perfectly identified (precision and recall 1.0). Our results suggest that insect flight sounds are potentially useful for detecting bees and quantifying their activity.

Mots clés: species classification; Hymenoptera; machine learning; acoustic analysis

Domaines: Sciences du Vivant [q-bio]

Satoshi Kawakita, Kotaro Ichikawa. Automated classification of bees and hornet using acoustic analysis of their flight sounds. Apidologie, 2019, 50 (1), pp.71-79. ⟨10.1007/s13592-018-0619-6⟩. ⟨hal-02436359⟩