And for my last trick today, I built this. Created another training set with specific attack types for each and tested things. Get 100% matching when running the training set back into itself.
I also created a testing set of samples, where I played 5 variations of each attack type and fed that in. The accuracy here varies greatly between the type of attack where some are more easily distinguishable than others but after running it for 1000 random examples I got an overall accuracy of 32.5%. So fairly dogshit. As I said, it varies for attack type, so some of these errors actually sound like each other, but I’m going to take this as a number to try and optimize with this training and testing sets of samples, to then generalize out from there.
I’ll try doing an MFCC-based version of this next and see if that fares any better, then perhaps try some of that macro-descriptor dimensionality reduction stuff.
