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Characterising soundscapes across diverse ecosystems using a universal acoustic feature set
Title: | Characterising soundscapes across diverse ecosystems using a universal acoustic feature set |
Authors: | Sethi, S Jones, NS Fulcher, B Picinali, L Clink, DJ Klinck, H Orme, CDLO Wrege, P Ewers, R |
Item Type: | Journal Article |
Abstract: | 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 eco-acoustic 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. |
Issue Date: | 21-Jul-2020 |
Date of Acceptance: | 10-Jun-2020 |
URI: | http://hdl.handle.net/10044/1/80904 |
DOI: | 10.1073/pnas.2004702117 |
ISSN: | 0027-8424 |
Publisher: | National Academy of Sciences |
Start Page: | 17049 |
End Page: | 17055 |
Journal / Book Title: | Proceedings of the National Academy of Sciences of USA |
Volume: | 117 |
Issue: | 29 |
Copyright Statement: | © 2020 The Author(s). Published under thePNAS license (https://www.pnas.org/authors/fees-and-licenses) |
Sponsor/Funder: | Rainforest Research Sdn Bhd World Wide Fund for Nature (WWF) Engineering & Physical Science Research Council (E Natural Environment Research Council (NERC) Engineering & Physical Science Research Council (EPSRC) Engineering & Physical Science Research Council (E |
Funder's Grant Number: | LBEE_P34395 UCL Ref: 542177 EP/K503733/1 NE/L012456/1 EP/N014529/1 EP/R511547/1 |
Keywords: | Science & Technology Multidisciplinary Sciences Science & Technology - Other Topics machine learning acoustic soundscape monitoring ecology BIG DATA INDEXES ECOLOGY ACCURACY acoustic ecology machine learning monitoring soundscape Acoustics Ecosystem Environmental Monitoring Firearms Forestry Machine Learning Sound Sound Spectrography Speech Sound Spectrography Speech Ecosystem Environmental Monitoring Acoustics Sound Forestry Firearms Machine Learning |
Publication Status: | Published |
Online Publication Date: | 2020-07-07 |
Appears in Collections: | Mathematics Applied Mathematics and Mathematical Physics Dyson School of Design Engineering Faculty of Natural Sciences Faculty of Engineering |