Real-time bioacoustics monitoring and automated species identification
By T. Mitchell Aide, C. Corrada-Bravo, Marconi Campos‐Cerqueira, Carlos Milan, Giovany Vega, R. Alvarez. PeerJ, 2013
420 citations recorded by the source corpus.
Traditionally, animal species diversity and abundance is assessed using a variety of methods that are generally costly, limited in space and time, and most importantly, they rarely include a permanent record. Given the urgency of climate change and the loss of habitat, it is vital that we use new technologies to improve and expand global biodiversity monitoring to thousands of sites around the world. In this article, we describe the acoustical component of the Automated Remote Biodiversity Monitoring Network (ARBIMON), a novel combination of hardware and software for automating data acquisition, data management, and species identification based on audio recordings. The major components of the cyberinfrastructure include: a solar powered remote monitoring station that sends 1-min recordings every 10 min to a base station, which relays the recordings in real-time to the project server, where the recordings are processed and uploaded to the project website (arbimon.net). Along with a module for viewing, listening, and annotating recordings, the website includes a species identification interface to help users create machine learning algorithms to automate species identification. To demonstrate the system we present data on the vocal activity patterns of birds, frogs, insects, and mammals from Puerto Rico and Costa Rica.
Subfields: Developmental Biology, Ecology, Ecology, Evolution, Behavior and Systematics
View Paper: https://www.semanticscholar.org/paper/e11246a8f9078e26a1dc4118650be830ffd5d897
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In Developmental Biology
In Ecology
In Ecology, Evolution, Behavior and Systematics
Citations in the corpus
- Applications for deep learning in ecology (2018, background): “Although this is a challenge in its own, the good news is, it is now relatively easy to gather hours and hours of acoustic recordings 63 .”
- Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring (2018, methodology): “Improved battery life and storage, on- board metadata collection and programmable schedules allow for extended autonomous deployments with flexible sampling regimes (Aide et al., 2013; Baumgartner et al., 2013).”
- Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge (2018, background): “, 2015), or onmachine learning algorithms (Aide et al., 2013).”