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Researchers develop an algorithm to analyze birdsong in a warming Arctic

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, National Science Foundation-funded researchers 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 birdsong 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 birdsongs 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 data set of animal vocalizations.

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