I like this a lot. I've been fascinated by Suzuki Toshitakas work on mapping bird sounds to syntax and want to experiment in that space too.
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.
ErroneousBosh 19 hours ago [-]
This is a bit off-the-wall but maybe you could use a variant of the approach used to see tiny movements in video? There you offset two clips by a frame and "subtract" them so that anything not moving just kind of vanishes, and purposeful movement shows up as a bright line.
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.
kiproping 1 days ago [-]
I wanted to do something similar to this, then I started doing some research on birds in general, and those in my locality, then I started learning about Audio and spectograms and Nyquist Theorem and many other interesting audio stuff.
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.
Thanks for sharing these resources and your story! I followed a very similar path, and ended up doing a biodiversity related MSc, with my dissertation being a custom classifier for poorly detected species in Príncipe. BirdNET and Perch are phenomenal achievements, but struggle in regions where, ironically, most of the world’s biodiversity is. What you’re doing for Rwandan species is so important!!
kiproping 8 hours ago [-]
thanks, how is your work going. Any interesting papers you did, what is an area you think needs more research.
is_true 1 days ago [-]
Do you think the same could be used for other cases? I'm thinking about detecting problems with cars (vehicles) just by the noise they make
kiproping 8 hours ago [-]
I know google has general sound classifiers like Yamnet, trained on youtube data but they are not very good for specific usecases. So you would have to create a custom model for you usecase.
Birdnet-go is really good and actively maintained. Shout out to tphakala.
rglover 1 days ago [-]
If you dig this idea, highly recommend the Cornell Merlin Bird ID app (likely built on the exact tech used here). Use it on my walks every week and it spots some wild stuff (it even correctly identifies birds who are being mocked by a Mockingbird). Helped me learn the Eastern Blue Bird call (personal favorite) so I know when they're nearby.
rozab 5 hours ago [-]
Apparently Merlin and BirdNET are entirely separate projects from different teams, although they both come from the Cornell Lab of Ornithology
brunohaid 1 days ago [-]
Excellent kachō-e prompt - working on something similar and found it hard to get the right balance between sharp outlines and watercolors, and especially plant morphology is dicey (eg plants like Cacao that fruit from the trunk instead of branch tips).
Did anyone come across projects that also nail that aspect well?
randogp 1 days ago [-]
Upvoted for the Theobroma cacao reference. Made me curious about what you are up to.
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.
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
- https://www.tensorflow.org/hub/tutorials/yamnet
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.