The BirdNET Appa free machine learning-powered tool that can identify more than 3,000 birds based on sound alone, generate reliable scientific data and make it easier for people to contribute citizen science data on birds simply by recording sounds, according to new research from Cornell .
“The most exciting part of this work is how easy 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 participation in citizen science,” which was published June 28 in PLOS Biology.
“You don’t need to know anything about birds, you just need a smartphone, and the BirdNET app can then give both you and the research team a prediction of which bird you’ve heard of,” Wood said. “This has led to massive participation worldwide, which translates into an incredible wealth of data. It really is a testament to an enthusiasm for birds that unites people from all walks of life.”
The study suggests that the BirdNET app lowers the barrier to citizen science because there are no skills to identify birds. Users simply listen to birds and tap the app to record. BirdNET uses artificial intelligence to automatically identify the species from sound and records the recording for use in research.
“Our guiding design principles were that we needed an accurate algorithm and a simple user interface,” said study co-author Stefan Kahl of the Yang Center of the Cornell Lab of Ornithology, who led the technical development. “Otherwise users would not return to the app.”
The results exceeded expectations: more than 2.2 million people have contributed data since its launch in 2018.
To test whether the app could generate reliable scientific data, the authors selected four test cases in the United States and Europe where conventional research had already provided robust answers. For example, their research shows that the BirdNET app data successfully replicated the known song type distribution pattern among white-throated sparrows, and the brown thrasher’s seasonal and migratory ranges.
Validating the reliability of the app data for research purposes was the first step in what the authors hope will be a long-term, global research effort — not just for birds, but ultimately for all wildlife and even entire soundscapes. The app is available for both iOS and Android platforms.
The BirdNET app is part of Cornell Lab of Ornithology’s suite of tools, including the educational Merlin Bird ID app and citizen science apps eBird, NestWatch and Project FeederWatch, which together have generated more than 1 billion bird observations, sounds and photos from participants around the world for use in science and conservation.
This project was supported by Jake Holshuh, the Arthur Vining Davis Foundations, the European Union, the European Social Fund for Germany and the German Federal Ministry of Education and Research.
Pat Leonard is a staff writer at the Cornell Lab of Ornithology.