A research team primarily based at NYU has achieved a breakthrough in ornithology and artificial intelligence by developing an end-to-end system to detect and identify the subtle nocturnal calls of migrating birds.
This advancement enhances bird migration tracking, potentially equipping conservationists with new data to bolster avian protection efforts.
In a recent paper published in IEEE Transactions on Audio, Speech and Language Processing, the researchers—from NYU, Cornell Lab of Ornithology and École Centrale de Nantes in France—present their BirdVoxDetect system, detailing the core machine learning algorithms that drive this innovative bird call detection technology. The paper caps off the team’s eight-year study of the topic.
"We're now able to extract incredibly subtle patterns from these audio recordings that the human ear might miss," said Juan Pablo Bello, the team lead.
Bello is an NYU professor with appointments in both the Tandon School of Engineering, where he is a professor of computing science and engineering, and the Steinhardt School of Culture, Education and Human Development, where he is a professor of music technology. He is the director of NYU Steinhardt's Music and Audio Research Lab and a member of NYU Tandon’s Center for Urban Science and Progress.
Read the full article originally posted by Tandon School of Engineering.
