AudioMoth: Evaluation of a smart open acoustic device for monitoring biodiversity and the environment

By A. Hill, Peter Prince, Evelyn Piña Covarrubias, C. Patrick Doncaster, Jake L. Snaddon, Alex Rogers. 2018

415 citations recorded by the source corpus.

The cost, usability and power efficiency of available wildlife monitoring equipment currently inhibits full ground‐level coverage of many natural systems. Developments over the last decade in technology, open science, and the sharing economy promise to bring global access to more versatile and more affordable monitoring tools, to improve coverage for conservation researchers and managers. Here we describe the development and proof‐of‐concept of a low‐cost, small‐sized and low‐energy acoustic detector: “AudioMoth.” The device is open‐source and programmable, with diverse applications for recording animal calls or human activity at sample rates of up to 384 kHz. We briefly outline two ongoing real‐world case studies of large‐scale, long‐term monitoring for biodiversity and exploitation of natural resources. These studies demonstrate the potential for AudioMoth to enable a substantial shift away from passive continuous recording by individual devices, towards smart detection by networks of devices flooding large and inaccessible ecosystems. The case studies demonstrate one of the smart capabilities of AudioMoth, to trigger event logging on the basis of classification algorithms that identify specific acoustic events. An algorithm to trigger recordings of the New Forest cicada (Cicadetta montana) demonstrates the potential for AudioMoth to vastly improve the spatial and temporal coverage of surveys for the presence of cryptic animals. An algorithm for logging gunshot events has potential to identify a shotgun blast in tropical rainforest at distances of up to 500 m, extending to 1 km with continuous recording. AudioMoth is more energy efficient than currently available passive acoustic monitoring devices, giving it considerably greater portability and longevity in the field with smaller batteries. At a build cost of ∼US$43 per unit, AudioMoth has potential for varied applications in large‐scale, long‐term acoustic surveys. With continuing developments in smart, energy‐efficient algorithms and diminishing component costs, we are approaching the milestone of local communities being able to afford to remotely monitor their own natural resources.

Subfields: Developmental Biology, Ecology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f3cfcd1712293c09a4051c6ba01d3b06665496ac

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Citations in the corpus

Passive acoustic monitoring as a potential tool to survey animal and ecosystem processes in freshwater environments (2019, background): “These can range from high quality, sophisticated commercial units by manufac‐ turers such as Wildlife Acoustics (Maynard, U.S.A.) or Frontier Labs (Brisbane, Australia), to affordable open source recorders for $50 (Hill et al., 2018).”
Detecting small changes in populations at landscape scales: a bioacoustic site-occupancy framework (2019, background): “ARUs reduce the need for trained observers and potentially persurvey costs, which could allow for more frequent and spatially extensive surveys than traditional observer-based approaches (Borker et al., 2015; Shonfield et al., 2018; Hill et al., 2018).”
Estimating bird density using passive acoustic monitoring: a review of methods and suggestions for further research (2021, background): “The recent development of low-cost recorders (Hill et al .”
Computational bioacoustics with deep learning: a review and roadmap (2021, neutral): “The AudioMoth is a popular example (Hill et al., 2018).”
Conservation technology: The next generation (2018, background): “…devices (e.g., smart nest box monitoring system, Zárybnická, Kubizňák, Šindelář, & Hlaváč, 2016; low-cost open-source programmable acoustic logger, Hill et al., 2018) to complex systems (e.g., large-scale realtime monitoring of elephant movement behaviour, Wall, Wittemyer, Klinkenberg, &…”
Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge (2018, background): “…and so with modern remote monitoring stations able to capture continuous audio recordings the prospect opens up of massive-scale spatio-temporal monitoring of birds (Aide et al., 2013; Furnas and Callas, 2015; Hill et al., 2017; Matsubayashi et al., 2017; Frommolt, 2017; Knight et al., 2017).”