Red-Team-Infrastructure-Wiki VS dejavu

I found this as a useful starting point: https://github.com/bluscreenofjeff/Red-Team-Infrastructure-Wiki

We have done it by connecting a Huawei LTE-Stick to a Raspberry Pi with Kali Linux. It automatically sets up a VPN connection to a control server in AWS where we have red teaming infrastructure set up. When we then connect the rpi into the network we can do some SSH port forwarding and connect to the rpi, which is now a network implant where we can do basically anything from. This sometimes requires finding Active Directory credentials around the office or through a phishing campaign (really easy with the access to the local network) or something, in order to get a foothold onto the AD infrastructure.

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.