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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
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.
1424-8220
Prince, Peter
13940cd1-98ab-4dca-a9ce-2403b2e61daa
Hill, Andrew
bfc05b70-7a90-40ab-8240-4d1f56aa3e4d
Pina-Covarrubias, Evelyn
a3202474-20c4-4a8c-b34d-713d8e060f0f
Doncaster, Charles
0eff2f42-fa0a-4e35-b6ac-475ad3482047
Snaddon, Jake
31a601f7-c9b0-45e2-b59b-fda9a0c5a54b
Rogers, Alex
60b99721-b556-4805-ab34-deb808a8666c
Prince, Peter
13940cd1-98ab-4dca-a9ce-2403b2e61daa
Hill, Andrew
bfc05b70-7a90-40ab-8240-4d1f56aa3e4d
Pina-Covarrubias, Evelyn
a3202474-20c4-4a8c-b34d-713d8e060f0f
Doncaster, Charles
0eff2f42-fa0a-4e35-b6ac-475ad3482047
Snaddon, Jake
31a601f7-c9b0-45e2-b59b-fda9a0c5a54b
Rogers, Alex
60b99721-b556-4805-ab34-deb808a8666c

Prince, Peter, Hill, Andrew, Pina-Covarrubias, Evelyn, Doncaster, Charles, Snaddon, Jake 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).

Record type: Article

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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More information

Submitted date: 16 January 2019
Accepted/In Press date: 24 January 2019
e-pub ahead of print date: 29 January 2019
Published date: 29 January 2019

Identifiers

Local EPrints ID: 428582
URI: http://eprints.soton.ac.uk/id/eprint/428582
ISSN: 1424-8220
PURE UUID: 2f0a76ff-3a21-4e9b-b0ec-38b8fa2f25b0
ORCID for Evelyn Pina-Covarrubias: ORCID iD orcid.org/0000-0002-3564-7467
ORCID for Charles Doncaster: ORCID iD orcid.org/0000-0001-9406-0693
ORCID for Jake Snaddon: ORCID iD orcid.org/0000-0003-3549-5472

Catalogue record

Date deposited: 01 Mar 2019 17:30
Last modified: 28 Oct 2022 02:12

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Contributors

Author: Peter Prince
Author: Andrew Hill
Author: Evelyn Pina-Covarrubias ORCID iD
Author: Jake Snaddon ORCID iD
Author: Alex Rogers

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