globalchange  > 气候变化与战略
DOI: 10.1073/pnas.2004702117
论文题名:
Characterizing soundscapes across diverse ecosystems using a universal acoustic feature set
作者: Sethi S.S.; Jones N.S.; Fulcher B.D.; Picinali L.; Clink D.J.; Klinck H.; Orme C.D.L.; Wrege P.H.; Ewers R.M.
刊名: Proceedings of the National Academy of Sciences of the United States of America
ISSN: 0027-8424
出版年: 2020
卷: 117, 期:29
起始页码: 17049
结束页码: 17055
语种: 英语
英文关键词: Acoustic ; Ecology ; Machine learning ; Monitoring ; Soundscape
Scopus关键词: article ; biodiversity ; convolutional neural network ; ecology ; ecosystem monitoring ; habitat quality ; human ; illegal timber ; sound ; acoustics ; classification ; ecosystem ; environmental monitoring ; firearm ; forestry ; machine learning ; procedures ; sound detection ; speech ; Acoustics ; Ecosystem ; Environmental Monitoring ; Firearms ; Forestry ; Machine Learning ; Sound ; Sound Spectrography ; Speech
英文摘要: Natural habitats are being impacted by human pressures at an alarming rate. Monitoring these ecosystem-level changes often requires labor-intensive surveys that are unable to detect rapid or unanticipated environmental changes. Here we have developed a generalizable, data-driven solution to this challenge using ecoacoustic data. We exploited a convolutional neural network to embed soundscapes from a variety of ecosystems into a common acoustic space. In both supervised and unsupervised modes, this allowed us to accurately quantify variation in habitat quality across space and in biodiversity through time. On the scale of seconds, we learned a typical soundscape model that allowed automatic identification of anomalous sounds in playback experiments, providing a potential route for real-time automated detection of irregular environmental behavior including illegal logging and hunting. Our highly generalizable approach, and the common set of features, will enable scientists to unlock previously hidden insights from acoustic data and offers promise as a backbone technology for global collaborative autonomous ecosystem monitoring efforts. © 2020 National Academy of Sciences. All rights reserved.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/164134
Appears in Collections:气候变化与战略

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作者单位: Sethi, S.S., Department of Mathematics, Imperial College London, London, SW7 2AZ, United Kingdom, Dyson School of Design Engineering, Imperial College London, London, SW7 2AZ, United Kingdom, Department of Life Sciences, Imperial College London, Ascot, SL5 7PY, United Kingdom; Jones, N.S., Department of Mathematics, Imperial College London, London, SW7 2AZ, United Kingdom; Fulcher, B.D., School of Physics, University of Sydney, Sydney, NSW 2006, Australia; Picinali, L., Dyson School of Design Engineering, Imperial College London, London, SW7 2AZ, United Kingdom; Clink, D.J., Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, NY 14850, United States; Klinck, H., Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, NY 14850, United States; Orme, C.D.L., Department of Life Sciences, Imperial College London, Ascot, SL5 7PY, United Kingdom; Wrege, P.H., Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, NY 14850, United States; Ewers, R.M., Department of Life Sciences, Imperial College London, Ascot, SL5 7PY, United Kingdom

Recommended Citation:
Sethi S.S.,Jones N.S.,Fulcher B.D.,et al. Characterizing soundscapes across diverse ecosystems using a universal acoustic feature set[J]. Proceedings of the National Academy of Sciences of the United States of America,2020-01-01,117(29)
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