For the 400+ sounds that Merlin (the user-friendly, offline-functional sister app to BirdNET) can now identify with 80-90% accuracy, it took a team of dozens of bird sound ID experts several years to annotate tens of thousands of individual audio spectrograms. On average, they needed about 1,000 recordings per species). Here is a bit more about how they did it: https://www.macaulaylibrary.org/2021/06/22/behind-the-scenes...
I work at the Cornell Lab, and we also have an app that is more consumer-oriented, and which DOESN'T require uploading the recordings to the cloud -- see Merlin Bird ID app https://merlin.allaboutbirds.org/
When the Lab’s researchers conceived of BirdNET, there were no reliable bird sound identification tools. BirdNET was built as a rapid prototype, engaging computer science students to build an app for that users to test the machine learning algorithms. BirdNET proved to be a research breakthrough and by 2020 was performing with far better accuracy than five other apps tested.
That success opened the way to apply computer vision to sound identification in the Lab’s outreach and education app, Merlin.
Merlin offers OFFLINE functionality, and multiple ways to help identify birds, including through a user describing the bird, taking a photo of the bird, and now recording a bird song or call. Merlin Bird ID is integrated with the Lab’s systems and resources, including updated taxonomy, bird information from eBird and Birds of the World, rich media from the Macaulay Library, life list building tools integrated with eBird, and more.