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.

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

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.)

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

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.

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 :-)

I think what makes Shazam magically "just work" is that they are searching for the exact embedding of the recorded version of the song - it doesn't work for a cover version, acapella, etc even if it is very similar. (Everyone would want an app that you can hum into and it will ID the song, but I guess it's a very tough problem.)

Merlin is solving the more general problem so makes sense that it's much more finicky and less accurate.

> an app that you can hum into and it will ID the song

YouTube Music and Google Search do OK with these types. Sometimes if I know a song well enough, I will open YT Music, activate the mic search, and sing a few bars, instead of typing the song title.

According to Gemini, SoundHound is also a good app for song-hummers to try.

Still, we're talking orders of magnitude here. I've fed Shazam with a stream of the cheapest lapel microphone you could get, mounted behind a rack filled with twelve very noisy servers, in a hall filled with 5000 people also all being noisy, and it would reliably recognize music from the other side of that hall before I could do so myself (I had to walk halfway there to hear “oh, yes, it's actually right”). And that's on a stream encoded with 9600bps GSM compression. With Merlin, I can hold out my phone on a silent day, hear the bird loud and clear and it will hardly register.

Shazam primarily identifies on the geometry of spectogram peaks, FWIW (I wrote my master's thesis on the DSP of music recognition back in the day; it's possible that they are doing something more fancy now, of course, but I'm not sure if they would want to). I don't know exactly what Merlin is doing.

I'm not sure why but this seems very phone dependent. I can stand next to another person, both running Merlin in silence, and mine will pick up much more than some people's phones. My phone is not particularly new (Zenfone 8).

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.