Automated Approach to Ecological Monitoring of Bird Species
In 2012, Researchers at Oregon State university created a new automated approach to ecological monitoring of bird species capable of identifying multiple bird sounds at one time. It is challenging for even the most skilled researchers to identify the species from the call of one bird, let alone several at one time, but this new technology provides an automated approach that is much more effective and efficient. It works by applying an algorithm in a multi-label, multi-instance framework (hence the name) that is able to sort and classify the recorded sounds to specific bird species. It also utilizes a 2D time-frequency segmentation for audio recordings, which allows for separate analysis of overlapping audio recordings. At present, this helps researchers identify which species are present and monitor how a given area’s diversity may be changing as a result of habitat loss or climate change, as birds are an important metric for the overall health of ecosystems. The future of this technology may be in identifying other species and sounds that do not come from birds, like the sound of a cricket or a felled tree, which would provide additional useful information for monitoring the health of species at risk and ecosystems threatened by habitat loss and climate change.
Oregon State University. (2012, May 31). “Singing in the rain: Technology improves monitoring of bird sounds.”
Briggs, Forrest, Balaji Lakshminarayanan, Lawrence Neal, Xiaoli Z. Fern, Raviv Raich, Sarah JK Hadley, Adam S. Hadley, and Matthew G. Betts. “Acoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach.”
Categories
Artificial Intelligence, Biodiversity, Data, Ecological Monitoring, Internet of Things, Monitoring
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