Next Article in Journal
Designing Paper-Based Immunoassays for Biomedical Applications
Next Article in Special Issue
Heavy Metal Soil Contamination Detection Using Combined Geochemistry and Field Spectroradiometry in the United Kingdom
Previous Article in Journal
EEG Classification of Motor Imagery Using a Novel Deep Learning Framework
Previous Article in Special Issue
An Artificial Intelligence Application for Post-Earthquake Damage Mapping in Palu, Central Sulawesi, Indonesia
Article Menu
Issue 3 (February-1) cover image

Export Article

Open AccessArticle
Sensors 2019, 19(3), 553;

Deploying Acoustic Detection Algorithms on Low-Cost, Open-Source Acoustic Sensors for Environmental Monitoring

School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
School of Biological Sciences, University of Southampton, Southampton SO17 1BJ, UK
School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
Department of Computer Science, University of Oxford, Oxford OX1 2JD, UK
Author to whom correspondence should be addressed.
Received: 17 December 2018 / Revised: 16 January 2019 / Accepted: 24 January 2019 / Published: 29 January 2019
Full-Text   |   PDF [1783 KB, uploaded 29 January 2019]   |  


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 an endangered cicada species, and also 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. View Full-Text
Keywords: acoustics; bioacoustics; ecology; conservation; machine learning acoustics; bioacoustics; ecology; conservation; machine learning

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Prince, P.; Hill, A.; Piña Covarrubias, E.; Doncaster, P.; Snaddon, J.L.; Rogers, A. Deploying Acoustic Detection Algorithms on Low-Cost, Open-Source Acoustic Sensors for Environmental Monitoring. Sensors 2019, 19, 553.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top