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Combining machine learning and a universal acoustic feature-set yields efficient automated monitoring of ecosystems
View ORCID ProfileSarab S. Sethi, Nick S. Jones, View ORCID ProfileBen D. Fulcher, View ORCID ProfileLorenzo Picinali, View ORCID ProfileDena J. Clink, View ORCID ProfileHolger Klinck, View ORCID ProfileC. David L. Orme, Peter H. Wrege, View ORCID ProfileRobert M. Ewers
doi: https://doi.org/10.1101/865980
Sarab S. Sethi
1Department of Mathematics, Imperial College London, London, UK
2Dyson School of Design Engineering, Imperial College London, London, UK
3Department of Life Sciences, Imperial College London, London, UK
Nick S. Jones
1Department of Mathematics, Imperial College London, London, UK
Ben D. Fulcher
4School of Physics, University of Sydney, Sydney, Australia
Lorenzo Picinali
2Dyson School of Design Engineering, Imperial College London, London, UK
Dena J. Clink
5Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, USA
Holger Klinck
5Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, USA
C. David L. Orme
3Department of Life Sciences, Imperial College London, London, UK
Peter H. Wrege
5Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, USA
Robert M. Ewers
3Department of Life Sciences, Imperial College London, London, UK
Posted December 05, 2019.
Combining machine learning and a universal acoustic feature-set yields efficient automated monitoring of ecosystems
Sarab S. Sethi, Nick S. Jones, Ben D. Fulcher, Lorenzo Picinali, Dena J. Clink, Holger Klinck, C. David L. Orme, Peter H. Wrege, Robert M. Ewers
bioRxiv 865980; doi: https://doi.org/10.1101/865980
Combining machine learning and a universal acoustic feature-set yields efficient automated monitoring of ecosystems
Sarab S. Sethi, Nick S. Jones, Ben D. Fulcher, Lorenzo Picinali, Dena J. Clink, Holger Klinck, C. David L. Orme, Peter H. Wrege, Robert M. Ewers
bioRxiv 865980; doi: https://doi.org/10.1101/865980
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