Deploying acoustic detection algorithms on low-cost, open-source acoustic sensors for environmental monitoring
Deploying acoustic detection algorithms on low-cost, open-source acoustic sensors for environmental monitoring
Conservation researchers require low-cost access to acoustic monitoring technology. However, affordable tools are often constrained to short-term studies due to high energy consumption and limited storage. To enable long-term monitoring, energy and space efficiency must be improved on such tools. This paper describes the development and deployment of three acoustic detection algorithms that reduce the power and storage requirements of acoustic monitoring on affordable, open-source hardware. The algorithms aim to detect bat echolocation, to search for evidence of a endangered cicada species, and to collect evidence of poaching in a protected nature reserve. The algorithms are designed to run on AudioMoth: a low-cost, open-source acoustic monitoring device, developed by the authors and widely adopted by the conservation community. Each algorithm addresses a detection task of increasing complexity – implementing extra analytical steps to account for environmental conditions such as wind, analysing samples multiple times to prevent missed events, and incorporating a hidden Markov model for sample classification in both the time and frequency domain. For each algorithm we report on real-world deployments carried out with partner organisations and also benchmark the hidden Markov model against a convolutional neural network, a deep-learning technique commonly used for acoustics. The deployments demonstrate how acoustic detection algorithms extend the use of low-cost, open-source hardware and facilitate a new avenue for conservation researchers to perform large-scale monitoring.
Prince, Peter
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Hill, Andrew
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Piña Covarrubias, Evelyn
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Doncaster, Patrick
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Snaddon, Jake L.
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Rogers, Alex
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29 January 2019
Prince, Peter
13940cd1-98ab-4dca-a9ce-2403b2e61daa
Hill, Andrew
bfc05b70-7a90-40ab-8240-4d1f56aa3e4d
Piña Covarrubias, Evelyn
a3202474-20c4-4a8c-b34d-713d8e060f0f
Doncaster, Patrick
0eff2f42-fa0a-4e35-b6ac-475ad3482047
Snaddon, Jake L.
31a601f7-c9b0-45e2-b59b-fda9a0c5a54b
Rogers, Alex
60b99721-b556-4805-ab34-deb808a8666c
Prince, Peter, Hill, Andrew, Piña Covarrubias, Evelyn, Doncaster, Patrick, Snaddon, Jake L. and Rogers, Alex
(2019)
Deploying acoustic detection algorithms on low-cost, open-source acoustic sensors for environmental monitoring.
Sensors, 19 (3), [553].
(doi:10.3390/s19030553).
Abstract
Conservation researchers require low-cost access to acoustic monitoring technology. However, affordable tools are often constrained to short-term studies due to high energy consumption and limited storage. To enable long-term monitoring, energy and space efficiency must be improved on such tools. This paper describes the development and deployment of three acoustic detection algorithms that reduce the power and storage requirements of acoustic monitoring on affordable, open-source hardware. The algorithms aim to detect bat echolocation, to search for evidence of a endangered cicada species, and to collect evidence of poaching in a protected nature reserve. The algorithms are designed to run on AudioMoth: a low-cost, open-source acoustic monitoring device, developed by the authors and widely adopted by the conservation community. Each algorithm addresses a detection task of increasing complexity – implementing extra analytical steps to account for environmental conditions such as wind, analysing samples multiple times to prevent missed events, and incorporating a hidden Markov model for sample classification in both the time and frequency domain. For each algorithm we report on real-world deployments carried out with partner organisations and also benchmark the hidden Markov model against a convolutional neural network, a deep-learning technique commonly used for acoustics. The deployments demonstrate how acoustic detection algorithms extend the use of low-cost, open-source hardware and facilitate a new avenue for conservation researchers to perform large-scale monitoring.
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sensors-19-00553
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Accepted/In Press date: 24 January 2019
e-pub ahead of print date: 29 January 2019
Published date: 29 January 2019
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Local EPrints ID: 428582
URI: http://eprints.soton.ac.uk/id/eprint/428582
ISSN: 1424-8220
PURE UUID: 2f0a76ff-3a21-4e9b-b0ec-38b8fa2f25b0
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Date deposited: 01 Mar 2019 17:30
Last modified: 12 Nov 2024 03:07
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Author:
Peter Prince
Author:
Andrew Hill
Author:
Evelyn Piña Covarrubias
Author:
Alex Rogers
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