The BirdNET app, a free machine-learning powered tool that can identify more than 3,000 birds by sound alone, generates reliable scientific data and makes it easier for people to contribute citizen-science data on birds by simply recording sounds, according to new Cornell research. "The most exciting part of this work is how simple it is for people to participate in bird research and conservation," said Connor Wood, research associate in the K. Lisa Yang Center for Conservation Bioacoustics at the Cornell Lab of Ornithology and lead author of The Machine Learning-Powered BirdNET App Reduces Barriers to Global Bird Research by Enabling Citizen Science Participation, which was published on 28 June in PLOS Biology. "You don't need to know anything about birds, you just need a smartphone, and the BirdNET app can then provide both you and the research team with a prediction for what bird you've heard," Wood said. "This has led to tremendous participation worldwide, which translates to an incredible wealth of data. It's really a testament to an enthusiasm for birds that unites people from all walks of life."