Bird populations are plummeting, thanks to logging, agriculture, and climate change. Scientists keep track of species by recording their calls, but even the best computer programs can’t reliably distinguish bird calls from other sounds. Now, thanks to a bit of crowdsourcing and a lot of artificial intelligence (AI), researchers say they have something to crow about.
AI algorithms can be as finicky as finches, often requiring manual calibration and retraining for each new location or species. So an interdisciplinary group of researchers launched the Bird Audio Detection challenge, which released hours of audio from environmental monitoring stations around Chernobyl, Ukraine, which they happened to have access to, as well as crowdsourced recordings, some of which came from an app called Warblr.
Humans labeled each 10-second clip as containing a bird call or not. Using so-called machine learning, in which computers learn from data, 30 teams trained their AIs on a set of the recordings for which labels were provided and then tested them on recordings for which they were not. Most relied on neural networks, a type of AI inspired by the brain that connects many small computing elements akin to neurons.