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Automated classification of bees and hornet using acoustic analysis of their flight sounds
- Source :
- Apidologie, Apidologie, Springer Verlag, 2019, 50 (1), pp.71-79. ⟨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
- Publication Year :
- 2019
- Publisher :
- HAL CCSD, 2019.
-
Abstract
- International audience; 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.
- Subjects :
- 0106 biological sciences
Sound (medical instrument)
biology
Vespa simillima
business.industry
[SDV]Life Sciences [q-bio]
Environmental sounds
Pattern recognition
biology.organism_classification
010603 evolutionary biology
01 natural sciences
Insect flight
Hymenoptera
010602 entomology
Bombus ardens
machine learning
acoustic analysis
Species level
Insect Science
Artificial intelligence
Mel-frequency cepstrum
species classification
Precision and recall
business
Subjects
Details
- Language :
- English
- ISSN :
- 00448435 and 12979678
- Database :
- OpenAIRE
- Journal :
- Apidologie, Apidologie, Springer Verlag, 2019, 50 (1), pp.71-79. ⟨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
- Accession number :
- edsair.doi.dedup.....4dcf07c797aa12bd048efc1be98e5f00