Yeah, that took me a bit to figure out. I guess it makes sense since more-often-than-not you want the distance to the knearest, but in this case I wanted a static ordering. In this case it’s easy enough to zl rev it, but I can see this getting much stickier if you want a > @numneighbors 2 version as I guess you’d need a matrix and some plumbing to reorder the knearestdist list based on the knearest list.
For this vid I just used the setup as I had, with data I had pre-analyzed. The tuning is ~20Hz diff (~430 to ~450) so not insignificant. Also using a different physical sensor in a different physical position (3 o’clock in the vid, vs 12 o’clock in the training). But yes, I agree, not great differentiation in that vid.
This is something I tested extensively back in the day (a ton in this thread, more recent optimization here for optimizing for an mlp regressor.
At the moment I’m using:
13 mfccs / startcoeff 1
zero padding (256 64 512)
min 200 / max 12000
mean std low high (1 deriv)
Which when comparing between center and edge training data gave me 96.9% accuracy. This was with the original Sensory Percussion hardware, which I’m now testing with a DIY version (much quieter, better dynamic range, wider freq response, etc…). It would be worthwhile revisiting the optimization with that new sensor.
(listen to the diff in these two clips, recorded at the same time with the official hardware first, then my DIY one second)
Sensor_Comparison.mp3.zip (708.5 KB)
Yeah, would love an old-fashioned geek out sesh if you’re down. I’ll be in the UK in a couple weeks and was planning on taking a trip to Hudds for one of the days, so maybe something then?
I was having a bit of a brain fart with this. I guess this is what I had in mind:
I had to do something similar with an update to my DIY fader thing last year and had forgotten what I had.
This sounds better though, and hopefully more robust.