A competition to identify bird calls using machine learning

June 16th, 2020

Do you hear the birds chirping outside your window? There are more than 10,000 bird species in the world, and they can be found in nearly every environment, from untouched rainforests to suburbs and cities. Birds play an essential role in nature. They are high up in the food chain and integrate changes occurring at low levels. As such, birds are excellent indicators of deteriorating habitat quality and environmental pollution. However, it’s often easier to hear birds than see them. With proper sound detection and classification, researchers could automatically intuit factors about an area’s quality of life-based on a changing bird population.

There are already many projects underway to extensively monitor birds by recording natural soundscapes over long periods. However the analysis of these datasets is often done manually, is painstakingly slow, and results are incomplete. Data science may be able to assist, so researchers have turned to large crowdsourced databases of vocal recordings of birds to train AI models.

To fully take advantage of these extensive and information-rich sound archives, researchers need good machine listeners to reliably extract as much information as possible to aid data-driven conservation.

In partnership with the Cornell Lab of Ornithology, Google’s bioacoustics team—part of ourAI for Social Good initiative—is announcing a competition to use machine learning to identify bird calls. In this competition, data scientists will identify a wide variety of bird vocalizations in soundscape recordings. Training audio comes from the Xeno-Canto project, a crowd-sourced collection of thousands of hours of bird sounds from around the world. We’re offering $25,000 in prizes for the best entries, and hosting the competition on Kaggle, the world’s largest data science competition community with more than 4 million members from 194 countries. The competition kicks off today and will last until September 2—check out the competition page for more details.

If successful, winners of this competition will help researchers better understand changes in habitat quality, levels of pollution, and the effectiveness of restoration efforts. The eventual conservation outcomes could greatly improve the quality of life for many living organisms—birds and human beings included.

Attribution for the image at the top of the post: Red-winged Blackbird © Drew Weber / Macaulay Library at the Cornell Lab of Ornithology (ML227768151)