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.
Highlights
Real-time analysis of data from acoustic sensors is increasingly becoming commonplace, with many research projects using low-cost micro-controllers and field-programmable gate arrays (FPGAs) to broadcast acoustic data to a central hub for analysis
There is increasing interest in the use of dedicated, power-efficient acoustic sensors that conservation researchers can use for large-scale, long-term monitoring of natural resources and anthropogenic impacts on the environment [5]
We present a full technical evaluation of three acoustic detection algorithms which use the AudioMoth platform for applications that aim to monitor the environment and protect biodiversity
Summary
Real-time analysis of data from acoustic sensors is increasingly becoming commonplace, with many research projects using low-cost micro-controllers and field-programmable gate arrays (FPGAs) to broadcast acoustic data to a central hub for analysis This technique has found a wide range of applications, from monitoring noise pollution in urban environments [1] to non-intrusively monitoring threatened bird species [2]. There is increasing interest in the use of dedicated, power-efficient acoustic sensors that conservation researchers can use for large-scale, long-term monitoring of natural resources and anthropogenic impacts on the environment (such as poaching) [5] The research in this area includes the development of self-powered sensors that use energy harvesting techniques such as solar panels [6] and triboelectric nanogenerators [7]. These charging methods are constrained by the amount of power they can produce in their operating environments, which may present suboptimal conditions for power generation
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