Newswise — Ithaca, NY—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. Results of tests to measure the app's accuracy are published in the open access journal PLOS Biology.

This new research from lead author Connor Wood and colleagues in the K. Lisa Yang Center for Conservation Bioacoustics at the Cornell Lab of Ornithology suggests that the BirdNET app lowers the barrier to citizen science because it doesn’t require bird-identification skills. Users simply listen for birds and tap the app to record. BirdNET uses artificial intelligence to automatically identify the species by sound and captures 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 in the Yang Center at the Cornell Lab, who led the technical development. "Otherwise, users would not return to the app."

The results exceeded expectations: Since its launch in 2018, more than 2.2 million people have contributed data.

To test whether the app could generate reliable scientific data, the authors selected four test cases in the United States and Europe in which conventional research had already provided robust answers. Their study shows, for example, that BirdNET app data successfully replicated the known distribution pattern of song-types among White-throated Sparrows, and the seasonal and migratory ranges of the Brown Thrasher.

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 most exciting part of this work is how simple it is for people to participate in bird research and conservation," Wood said. "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. 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."
The BirdNET app is part of the 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, European Union, European Social Fund for Germany, and German Federal Ministry of Education and Research. Our work in the K. Lisa Yang Center for Conservation Bioacoustics is made possible by the generosity of K. Lisa Yang to advance innovative conservation technologies to inspire and inform the conservation of wildlife and habitats. 
Connor M. Wood, Stefan Kahl, Ashakur Rahaman, and Holger Klinck, 2022. "The machine learning-powered BirdNET App reduces barriers to global bird research by enabling citizen science participation.” PLOS Biology. 


Journal Link: PLOS Biology