气候变化领域集成服务门户(CCPortal): Characterizing soundscapes across diverse ecosystems using a universal acoustic feature set

CCPortal

DOI	10.1073/pnas.2004702117
	Characterizing soundscapes across diverse ecosystems using a universal acoustic feature set
	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.
发表日期	2020
ISSN	0027-8424
起始页码	17049
结束页码	17055
卷号	117 期号:29
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.
英文关键词	Acoustic; Ecology; Machine learning; Monitoring; Soundscape
语种	英语
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
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/160886
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, Cornel...
推荐引用方式 GB/T 7714	Sethi S.S.,Jones N.S.,Fulcher B.D.,et al. Characterizing soundscapes across diverse ecosystems using a universal acoustic feature set[J],2020,117(29).
APA	Sethi S.S..,Jones N.S..,Fulcher B.D..,Picinali L..,Clink D.J..,...&Ewers R.M..(2020).Characterizing soundscapes across diverse ecosystems using a universal acoustic feature set.Proceedings of the National Academy of Sciences of the United States of America,117(29).
MLA	Sethi S.S.,et al."Characterizing soundscapes across diverse ecosystems using a universal acoustic feature set".Proceedings of the National Academy of Sciences of the United States of America 117.29(2020).