Then I started going through the Intro to Conservation Bioacoustics by Cornell course, and started watching Bioacoustic Talks by the K. Lisa Yang Center cornell center.
And now I am almost at the point where I cant start manually tagging audio sets, for target species so that I can train custom classifiers to identify birds in Rwanda which are poorly detected by birdnet.
TLDR: Being jobless can lead you into interesting ventures.
* Nyquist Theorem. https://www.youtube.com/watch?v=IZJQXlbm2dU
* Intro to Conservation Bioacoustics https://www.birds.cornell.edu/ccb/pam-materials
* Bioacoustic Talks https://www.youtube.com/@CornellSounds
I think sound + location could be really interesting, because you can filter parts of the car that could be making noises that are similar knowing where the mic is.
Ive been assisting at a wild bird rehab but not until I got pet birds (released pet birds that no owner could be found for) did I realize they make these extremely faint sounds to each other that I can sometimes just barely make out when I'm right next to them but are not the other quiet humming they make.
My mic can capture those sounds sometimes, but I don't know how to analyze for example 24h of recording in the cage to find slight variations to background noise. It doesn't have to be real-time and not bird specific (want to capture sounds they make that doesn't register as bird in the models).
If anyone has a suggestion please point me in any direction you know of. Audio is pretty new for me.
Maybe doing something similar with a spectrogram would work? Two spectrograms, one delayed slightly with respect to the other, and subtract one from the other, and you might see bright spots that appear where the sound changes.
There's also an excellent alternative to BirdNet-Pi that runs well on non-Raspberry-Pi machines: https://github.com/tphakala/birdnet-go
Did anyone come across projects that also nail that aspect well?
Unsure if that is a valid assumption, docs could improve here.
