Real-time bioacoustics monitoring and automated species identification
Open accessAbstract
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.
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Cloud-based platforms such as EcoSounds-web and ARBIMON, together with annotation-focused tools like Whombat, enable collaborative annotation, archiving, and querying of large acoustic datasets.
“Cloud-based platforms such as EcoSounds-web and ARBIMON, together with annotation-focused tools like Whombat, enable collaborative annotation, archiving, and querying of large acoustic datasets”
From Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation (2026) · cites this paper as Aide et al. 2013
Cloud-linked monitoring systems beam recordings from the field to centralized servers; platforms like ARBIMON transmit recordings every few minutes, allowing near real-time species identification and event detection.
“Platforms like ARBIMON transmit recordings every few minutes, allowing near real-time species identification and event detection”
From Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation (2026) · cites this paper as Aide et al. 2013
By streaming sound and processing it locally with edge AI, real-time monitoring systems alert rangers to gunshots, chainsaws, or endangered species calls within minutes, enabling rapid intervention.
“By streaming sound and processing it locally with edge AI, these systems alert rangers to gunshots, chainsaws, or endangered species calls within minutes, enabling rapid intervention (Aide et al. 2013; Wrege et al. 2017).”
From Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation (2026) · cites this paper as Aide et al. 2013
Real-time monitoring networks are no longer just experimental; they are beginning to reshape conservation practice.
“Real-time monitoring networks (see Sect. 3.1) are no longer just experimental—they are beginning to reshape conservation practice.”
From Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation (2026) · cites this paper as Aide et al. 2013
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“…ARBIMON, together with annotation-focused tools like Whombat, enable collaborative annotation, archiving, and querying of large acoustic datasets (Aide et al. 2013; K. F. Darras et al. 2024; Martínez Balvanera et al. 2025 (Becker et al. 2018; Gupta et al. 2021); autoencoder-based clustering of…”
- Authors
- Aide, T. M., Corrada-Bravo, C., Campos-Cerqueira, M., Milan, C., Vega, G., & Alvarez, R.
- Affiliations
- University of Puerto Rico at Río Piedras (PR)
- Department of Biology, University of Puerto Rico-Rio Piedras, San Juan, Puerto Rico, United States
- Corresponding author
- Aide, T. Mitchell
- ORCID
- Aide, T. Mitchell
- Venue
- PeerJ
- Publisher
- PeerJ
- Published
- 2013-07-16
- Volume
- 1
- Pages
- e103
- ISSN
- 2167-8359 (ISSN-L)
- PMC
- PMC3719130
- OpenAlex
- W2072814159
- Cited by
- 487
- References
- 41
- Citations by year
- 2026: 42 · 2025: 36 · 2024: 21 · 2023: 31 · 2022: 42 · 2021: 49 · 2020: 42 · 2019: 33 · 2018: 95 · 2017: 40 · 2016: 24 · 2015: 18 · 2014: 12 · 2013: 2
- FWCI
- 14.27
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- PeerJ (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- WBI Studies Repository (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Defense
- Vedecká Grantová Agentúra MŠVVaŠ SR a SAV
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Developmental Biology › Animal Vocal Communication and Behavior
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Keywords
- automated species identification, acoustic remote sensing, real-time data transmission, bird vocalizations, frog vocalization, insect sounds
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