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

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.
Authors: Peter Prince, Andrew Hill, Evelyn Piña Covarrubias, Patrick Doncaster, Jake L. Snaddon and Alex Rogers
 
Journal: Sensors 2019, 19(3), 553
 
Publisher: MDPI 
 
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 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.

AudioMoth: a low-cost, low-power acoustic monitoring device developed for a wide variety of conservation projects, deployed on a tree in a grip-sealed bag using a cable tie. (credits: Peter Prince, Andrew Hill, Evelyn Piña Covarrubias, Patrick Doncaster, Jake L. Snaddon and Alex Rogers)

Photo: AudioMoth: a low-cost, low-power acoustic monitoring device developed for a wide variety of conservation projects, deployed on a tree in a grip-sealed bag using a cable tie. (credits: Peter Prince, Andrew Hill, Evelyn Piña Covarrubias, Patrick Doncaster, Jake L. Snaddon and Alex Rogers)

 
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).
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