BibTex format
@article{Sethi:2020:10.1073/pnas.2004702117,
author = {Sethi, S and Jones, NS and Fulcher, B and Picinali, L and Clink, DJ and Klinck, H and Orme, CDLO and Wrege, P and Ewers, R},
doi = {10.1073/pnas.2004702117},
journal = {Proceedings of the National Academy of Sciences of USA},
pages = {17049--17055},
title = {Characterising soundscapes across diverse ecosystems using a universal acoustic feature set},
url = {http://dx.doi.org/10.1073/pnas.2004702117},
volume = {117},
year = {2020}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - 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.
AU - Sethi,S
AU - Jones,NS
AU - Fulcher,B
AU - Picinali,L
AU - Clink,DJ
AU - Klinck,H
AU - Orme,CDLO
AU - Wrege,P
AU - Ewers,R
DO - 10.1073/pnas.2004702117
EP - 17055
PY - 2020///
SN - 0027-8424
SP - 17049
TI - Characterising soundscapes across diverse ecosystems using a universal acoustic feature set
T2 - Proceedings of the National Academy of Sciences of USA
UR - http://dx.doi.org/10.1073/pnas.2004702117
UR - http://hdl.handle.net/10044/1/80904
VL - 117
ER -