LOIC VS dejavu

Good morning what tool do you use to test your infra in terms of http load ? A tool that works, I tested : - https://github.com/tsenart/vegeta but it returns 0 errors or a http_net error from Golang - LOIC (Low Orbit Ion Canon) https://github.com/NewEraCracker/LOIC but the requests do not appear in my nginx logs and I feel no slowdown - Apache Jmeter https://jmeter.apache.org/ but I can't drop my infra and I have Java socket closed errors - K6 https://k6.io/ but I can't bring down my infra with - wrk https://github.com/wg/wrk no matter what parameter I put it doesn't make enough requests per second, I put the same parameters as on a tutorial and I don't get the same result...

In the meantime you could take a look at this DoS tool, which may help but really can't suggest any tool that can be extremely easy to use for someone who doesn't know about this stuff.

* https://github.com/NewEraCracker/LOIC

GitHub page for the project if you want to verify what it is: https://github.com/NewEraCracker/LOIC

Low Orbit Ion Canon (LOIC) - Open source network stress tool written for Windows.

They are downloadable!

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.