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Automated classification of bees and hornet using acoustic analysis of their flight sounds Automated classification of bees and hornet using acoustic analysis of their flight sounds 川北, 哲史 カワキタ, サトシ KAWAKITA, Satoshi The Author(s) species classification Hymenoptera machine learning acoustic analysis species classification Hymenoptera machine learning acoustic analysis 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. Springer Nature 2019-01-07 eng journal article AM https://repository.naro.go.jp/records/3378 10.1007/s13592-018-0619-6 SC10201903130002 NARO成果DBa author manuscript このアーカイブは著者版です。出版社版ではありません。引用の際には出版社版をご利用ください。 This is not the published version. Please cite only the published version. This is a post-peer-review, pre-copyedit version of an article published in [Apidologie]. The final authenticated version is available online at: https://doi.org/10.1007/s13592-018-0619-6 https://doi.org/10.1007/s13592-018-0619-6 Apidologie 1297-9678 Apidologie Apidologie 50 71 79 https://repository.naro.go.jp/record/3378/files/SC10201903130002_PostPrint-a.pdf application/pdf 391.3 kB 2020-02-08