The University of Southampton
University of Southampton Institutional Repository

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
11128d21-ddb4-4f07-b9e6-cd5abf2e83bc
Doncaster, Charles
0eff2f42-fa0a-4e35-b6ac-475ad3482047
Snaddon, Jake
31a601f7-c9b0-45e2-b59b-fda9a0c5a54b
Rogers, Alex
e60d4ae1-78da-4b4c-9dd7-dac5c46a9405
Prince, Peter
13940cd1-98ab-4dca-a9ce-2403b2e61daa
Hill, Andrew
bfc05b70-7a90-40ab-8240-4d1f56aa3e4d
Pina Covarrubias, Evelyn
11128d21-ddb4-4f07-b9e6-cd5abf2e83bc
Doncaster, Charles
0eff2f42-fa0a-4e35-b6ac-475ad3482047
Snaddon, Jake
31a601f7-c9b0-45e2-b59b-fda9a0c5a54b
Rogers, Alex
e60d4ae1-78da-4b4c-9dd7-dac5c46a9405

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). (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.

Text
sensors-19-00553 - Version of Record
Available under License Creative Commons Attribution.
Download (1MB)

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: https://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: 10 Dec 2019 01:54

Export record

Altmetrics

Contributors

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

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×