AI to
analyze field recordings and estimate songbird arrivals
Date: June 20, 2018
Source: Lamont-Doherty Earth Observatory, Columbia
University
Summary:
Researchers
describe a way to quickly sift through thousands of hours of field recordings
to estimate when songbirds arrive at their Arctic breeding grounds. Their
research could be applied to any dataset of animal vocalizations to understand
how migratory animals are responding to climate change.
Spring is
coming earlier to parts of the Arctic, and so are some migratory birds. But
researchers have yet to get a clear picture of how climate change is
transforming tundra life. That's starting to change as automated tools for
tracking birds and other animals in remote places come online, giving
researchers an earful of clues about how wildlife is adapting to hotter
temperatures and more erratic weather.
In a new
study in Science Advances, researchers at Columbia University describe a
way to quickly sift through thousands of hours of field recordings to estimate
when songbirds reached their breeding grounds on Alaska's North Slope. They
trained an algorithm on a subset of the data to pick out bird song from wind,
trucks and other noise, and estimate, from the amount of time the birds spent
singing and calling each day, when they had arrived en masse.
The
researchers also turned the algorithm loose on their data with no training to
see if it could pick out bird songs on its own and approximate an arrival date.
In both cases, the computer's estimates closely matched what human observers
had noted in the field. Their unsupervised machine learning method could
potentially be extended to any dataset of animal vocalizations.
"Our
methods could be retooled to detect the arrival of birds and other vocal
animals in highly seasonal habitats," said the study's lead author, Ruth
Oliver, a graduate student at Columbia. "This could allow us to track
largescale changes in how animals are responding to climate change."
No comments:
Post a Comment