Over 10,000 bird species occur in the world, and they can be found in nearly every environment, from untouched rainforests to suburbs and even cities. Birds play an essential role in nature. They are high up in the food chain and integrate changes occurring at lower levels. As such, birds are excellent indicators of deteriorating habitat quality and environmental pollution. However, it is 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 continuously recording natural soundscapes over long periods. However, as many living and nonliving things make noise, the analysis of these datasets is often done manually by domain experts. These analyses are painstakingly slow, and results are often incomplete. Data science may be able to assist, so researchers have turned to large crowdsourced databases of focal recordings of birds to train AI models. Unfortunately, there is a domain mismatch between the training data (short recording of individual birds) and the soundscape recordings (long recordings with often multiple species calling at the same time) used in monitoring applications. This is one of the reasons why the performance of the currently used AI models has been subpar.
To unlock the full potential 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.
The Cornell Lab of Ornithology’s Center for Conservation Bioacoustics (CCB)’s mission is to collect and interpret sounds in nature. The CCB develops innovative conservation technologies to inspire and inform the conservation of wildlife and habitats globally. By partnering with the data science community, the CCB hopes to further its mission and improve the accuracy of soundscape analyses.
This project aims to identify a wide variety of bird vocalizations in soundscape recordings. Due to the complexity of the recordings, they contain weak labels. There might be anthropogenic sounds (e.g., airplane overflights) or other bird and non-bird (e.g., chipmunk) calls in the background, with a particular labeled bird species in the foreground.
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