Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2013 Jul 16:1:e103.
doi: 10.7717/peerj.103. Print 2013.

Real-time bioacoustics monitoring and automated species identification

T Mitchell Aide  1 , Carlos Corrada-Bravo, Marconi Campos-Cerqueira, Carlos Milan, Giovany Vega, Rafael Alvarez
Affiliations

Real-time bioacoustics monitoring and automated species identification

T Mitchell Aide et al. PeerJ. .

Abstract

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.

Keywords: Acoustic monitoring; Animal vocalization; Long-term monitoring; Machine learning; Species-specific algorithms.

PubMed Disclaimer

Figures

Figure 1
Figure 1. Workflow of data acquisition, processing, and management.
Figure 2
Figure 2. The ARBIMON-acoustic web-based tools for creating, testing, and applying the species-specific identification models.
Figure 3
Figure 3. Vocal activity in Sabana Seca.
Daily (A–C) and monthly (D–F) vocal activity of three species from Sabana Seca, Puerto Rico. The number in parenthesis is the number of recordings where the species was detected by the model. The detection frequency was calculated as the number of recordings with a positive detection divided by the total number of recordings during the time period.
Figure 4
Figure 4. Vocal activity in La Selva.
Daily vocal activity of six species from La Selva Biological Station, Costa Rica. The number in parenthesis is the number of recordings where the species was detected by the model. The detection frequency was calculated as the number of recordings with a positive detection divided by the total number of recordings during the time period.
See this image and copyright information in PMC

References

    1. Acevedo MA, Corrada-Bravo CJ, Corrada-Bravo H, Villanueva-Rivera LJ, Aide TM. Automated classification of bird and amphibian calls using machine learning: a comparison of methods. Ecological Informatics. 2009;4:206–214. doi: 10.1016/j.ecoinf.2009.06.005. - DOI
    1. Acevedo M, Villanueva-Rivera L. Using automated digital recording systems as effective tools for the monitoring of birds and amphibians. Wildlife Society Bulletin. 2006;34:211–214. doi: 10.2193/0091-7648(2006)34[211:UADRSA]2.0.CO;2. - DOI
    1. Anderson SE, Dave AS, Margoliash D. Template-based automatic recognition of birdsong syllables from continuous recordings. The Journal of the Acoustical Society of America. 1996;100:1209–1219. doi: 10.1121/1.415968. - DOI - PubMed
    1. Baum LE, Petrie T, Soules G, Weiss N. A maximization technique occurring in the statistical analysis of probabilistic functions of Markov Chains. The Annals of Mathematical Statistics. 1970;41:164–171. doi: 10.1214/aoms/1177697196. - DOI
    1. Brandes ST. Automated sound recording and analysis techniques for bird surveys and conservation. Bird Conservation International. 2008;18:163–173.