Characterizing soundscapes across diverse ecosystems using a universal acoustic feature set

Author:

Sethi Sarab S.ORCID,Jones Nick S.,Fulcher Ben D.ORCID,Picinali LorenzoORCID,Clink Dena JaneORCID,Klinck HolgerORCID,Orme C. David L.ORCID,Wrege Peter H.ORCID,Ewers Robert M.

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.

Funder

World Wildlife Fund

Sime Darby Foundation

RCUK | Natural Environment Research Council

RCUK | Engineering and Physical Sciences Research Council

Fulbright Association

Cornell Center for Conservation Bioacoustics

Project Jansoon

DOI | U.S. Fish and Wildlife Service

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

Reference56 articles.

1. Accuracy of acoustic respiration rate monitoring in pediatric patients;Patino;Paediatr. Anaesth.,2013

2. Continuous acoustic monitoring of grouted post-tensioned concrete bridges;Cullington;NDT Int.,2001

3. A. Harma , M. F. McKinney , J. Skowronek , “Automatic surveillance of the acoustic activity in our living environment” in 2005 IEEE International Conference on Multimedia and Expo (IEEE, Amsterdam, 2005), p. 4.

4. Applications of time-frequency analysis to signals from manufacturing and machine monitoring sensors;Atlas;Proc. IEEE,1996

5. Beyond Global Warming: Ecology and Global Change

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