Funnily enough I was in the bathroom (that's where I do my most random thinking) just yesterday evening thinking how template matching in computer vision, e.g. https://docs.opencv.org/5.0/py_tutorials/py_imgproc/py_templ... is basically audio signal processing but instead of being temporal it is spatial. Different signals but same principle.

Merlin got me into birding, and I've found this to be true, at least in some regions, especially if there's lots of background noise.

Unless a species has a really, really unique call or song, I've made it my personal rule to get eyes on it and, if I'm still not convinced, to take a picture and cross reference with iNaturalist and what folks on there think of it.

I've learned to curse the species that differ from others by the sheen of the backs of the males' necks or the angle of a wingbar. Also, gulls that only differ by things like the color of their feet or beaks.

For me, mostly a lot of false negatives. I have a Pixel 8, and I have no problems using the microphone normally, but somehow, Merlin requires the bird to be within a couple of meters of me and sing for quite a while before Merlin will even say “hearing a bird”. And of course, you have to be dead silent; if anyone talks or coughs, it will mess up the spectogram badly.

Given that it's colloquially “Shazam for birds” and Shazam is just amazingly resistant to noise, it's a bit disappointing :-)

You can usually tell, though. Rare birds in the wrong place or time that come up only once are problematic, but if the bird is detected repeatedly there's a better chance. You should also be on the lookout for birds that mimic other birds (like the northern mockingbird and the gray catbird.)

Occasionally I'll see a screenshot of Merlin with, I dunno, a dozen different birds, and in the middle of the list: a catbird. I never have the heart to tell people that they just heard one very creative bird.

Indeed - background noise will tend to lead to a lot more false positives, but you should use it as a guide rather than inherently trusting results. Same goes for using BirdNET-go and other models, though these do allow you to be very strict with detections and reduce false positives more.

Cornell / Merlin / eBird publishes a global species frequency DB indexed by location and date (so, how likely is a species to appear in a place, on a day of the year)... In a project that I was working on, this helped a lot when used to influence the "raw" identification based on appearance alone.

For info, there's a FOSS bird-call app, available on F-Droid, that's pretty decent: WhoBird

I've not seen this personally, but maybe it's region specific.

Love that documentary.

Tawny owls make so many different noises. The classic sound is two, one calls and one answers but they are very chatty with different sounds. They live all around, but never seen one.

I've got 33. I don't know how that stacks up against other but a large percentage of them came just from my backyard. Just got added a Baltimore Oriole that showed up this week.