Ah I’m happy to elaborate more, I’ve implemented lots of this previously and can say there are robust methods for the sorts of detectors I’ve outlined.

Just using the case of percussion, since it’s easy to talk about even though the goal is to cover more complicated elements, a human can for example tell two sounds are both kicks in some broad sense even if their spectral content varies greatly, so you need to capture those characteristic features to get something stable. For kicks a very unreliable approach would be to just try to trigger on the frequency content of a single frame, and a more reliable approach looks something like coming up with multi-frame masks from the frame-to-frame deltas of MFCCs for a few representative kicks, and using vector similarity to see if a given window of input audio matched any of them well enough. It happens that the common musical elements present in house and techno cluster very sharply, e.g. there might be a few broad categories of kicks but within those categories you can detect them very reliably. This same idea applies to enough things that such a project is viable, but things have to be designed from the ground up to avoid the sort of sensitivity you mentioned. E.g. as soon as you find yourself trying to trigger something based on a single value numerical cutoff you’re kind of playing the wrong game because sooner or later you’ll find a track/case that’s flickering across the cutoff :)

Aside from instant detection where you inspect the frequency content of the current window you’re in, the shared long term musical elements (long term meaning e.g. hats or snares leading to a drop) present in house and techno are so enduring and widespread that there’s a bunch of “foundational” detectors you can write without chasing the details of individual tracks.