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

Publication Type:Journal Article
Year of Publication:2019
Authors:Kawakita, Ichikawa
Date Published:Jul-01-2019
Parole chiave:acoustic analysis, Hymenoptera, machine learning, species classification

To 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.

Short Title:Apidologie
BioAcoustica ID: 
Scratchpads developed and conceived by (alphabetical): Ed Baker, Katherine Bouton Alice Heaton Dimitris Koureas, Laurence Livermore, Dave Roberts, Simon Rycroft, Ben Scott, Vince Smith