AI to analyze field recordings and estimate songbird arrivals
Date: June 20, 2018
Source: Lamont-Doherty Earth Observatory, Columbia University
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."