01631nas a2200181   4500008004100000022001400041245009500055210006900150260001600219520103000235653002201265653001601287653002101303653002701324100002201351700002101373856005501394       2019                        eng d  a0044-843500aAutomated classification of bees and hornet using acoustic analysis of their flight sounds0 aAutomated classification of bees and hornet using acoustic analy  cJul-01-20193 a<p>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&ndash;1.00). At the species level, our approach could classify the insect species with relatively high recalls and precisions (0.7&ndash;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.</p>
10aacoustic analysis10aHymenoptera10amachine learning10aspecies classification1 aKawakita, Satoshi1 aIchikawa, Kotaro  uhttp://link.springer.com/10.1007/s13592-018-0619-6