Osintgram VS dejavu

If the tattoo studios aren't necessary to have been from facebook specifically, Osintgram is a pretty effective tool for scraping shit from Instagram really quickly that could theoretically be used to achieve this, if not perhaps in a roundabout sort of way like starting from one business you know and getting shit from their friends' info and so on. I could swear that I had known about a similar tool for facebook, but I'm drawing a blank right now...

Doing this manually has many advantages, but you can, of course, use Osintgram for speedy results.

Github: https://github.com/Datalux/Osintgram

If I remember correctly Osintgram can achieve this

Since the original version is more or less inactive, I took the initiative to continue with the developments.

This doesn't seem hard, you can use something like this to dwoanload the songs: https://stackoverflow.com/a/27481870/6151784 and something like this to calculate how much they match: https://github.com/worldveil/dejavu The question is would you create a (dedicated) server to do your work? Or your own pc? You could also create a very simple page where someone would paste you a YouTube profile URL and you would check all songs of this URL. Also to have a db and save information about the matching and which youtube profiles have alsready been checked. Something like that could work.

If you read the installation.md file it clearly states that it has only been tested on UNIX systems, so you might be on your own trying to get it to wor in windows.

Using neural networks seems complicated, did you tried audio fingerprinting? I have been using this audio fingerprinting library to power this anime song synchronization script. You can check Panako and dejavu too.

I want to say these few seconds match these few seconds from a different audio track. Using dejavu raw has overhead I do not need/want and hence I've been fiddling around with the fingerprint script. When modifying the global variables I can get better hits or worse hits, I will admit that even after reading there recommended article and many other sources, I can't find some good explanations about the mathematics behind the filtering after the specgram has been applied. As far as a I am aware we first apply filters to find/make fine points across the spectrogram after that we only check the distance between points along the time axis not the frequency or a hypotenuse (weird).

I used a repo called https://github.com/worldveil/dejavu to compare audio hashed fingerprints and distinguish the difference between them.