The BirdNET app, a free machine learning-powered tool that can identify more than 3,000 birds based on sound alone, generates reliable scientific data and makes it easier for people to contribute citizen science data about birds by simply recording sounds.
An article that will be published on June 28 in the open access journal PLOS Biology by Connor Wood and colleagues at the K. Lisa Yang Center for Conservation Bioacoustics of the Cornell Lab of Ornithology, USA, suggests that the BirdNET app lowers the barrier to citizen science because there are no skills to identify birds to participate. Users simply listen to birds and tap the app to record. Using BirdNET artificial intelligence to automatically identify the species from sound and record the recording for use in research.
“Our guiding design principles were that we needed an accurate algorithm and a simple user interface,” said co-author Stefan Kahl in the Yang Center of the Cornell Lab, 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 which conventional research had already provided robust answers. Their results show that the data from the BirdNET app successfully replicated known patterns of song dialects in North American and European songbirds and accurately bird migration on both continents.
Validating the reliability of the app data for research purposes was the first step in what they hope will be a long-term, global research effort — not just for birds, but ultimately for all wildlife and even entire soundscapes. The data used in the four test cases is publicly available and the authors are working to make the entire dataset open.
“The most exciting part of this work is how easy it is for people to participate in bird research and conservation,” Wood adds. “You don’t need to know anything about birds, you just have a… smartphone, and the BirdNET app can then provide both you and the research team with a prediction for which bird you’ve heard of. This has led to huge 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 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.
The machine learning-powered BirdNET app reduces barriers to global bird research by enabling citizen science participation, PLoS Biology (2022). DOI: 10.1371/journal.pbio.3001670
Public Library of Science
Quote: Identifying bird species based on sound, an app opens new avenues for citizen science (2022, June 28), retrieved June 28, 2022 from https://phys.org/news/2022-06-bird-species-app- avenues-citizen. html
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