Ok, did some testing with acoustic kit sounds.
I recorded everything with a DPA 4060 magnetically clipped to the snare, kind of above the kick.
I hit each drum at a variety of dynamics and created sets with snares on and off.
Here is a “hybrid” of kick being trained with snares off, and snare being trained with snares on:
The differentiation between kick and snare being subtle came as a surprise. I imagine this is due to the FFT size (128/64). The hihat also came as a surprise. I initially recorded hihat hits that went from tightly closed to semi-open, and when I saw the dict for the hat, I used only from tight to closed, leaving out the semi-open hits for another set.
I then trained everything with the snares on. I don’t know if it’s because my playing was different, but the kick looks more differentiated here:
Next I tried training everything using a “complete” set. As in, training the kick with snares off and on, then doing the same for the snare. I also included the complete set of hits for the hat:
Surprisingly the snare didn’t change much. And the kick is quite similar to the kick being trained with snares on only. The hat looks a bit better here, less “broadband”.
And finally I created a preseed-refined dict. I used the “complete” set from above, and then ran @filterupdate 1
with a recording of me playing a beat with the same recording setup:
This looks the best, with clear differences between the hits, which makes sense.
In terms of how they track and sound. None of them worked super great (with the default ‘polyphonic’ patch). I didn’t spend a long time with each, as my focus/intent for today was just to create the dicts which I could then go back and more thoroughly test and compare. So that being said, I only really played back a bit of my recorded beat and messed with the thresholds at the bottom of the patch. It was difficult to find a setting that got minimal cross talk between kick and hat with the snare still working.
I also didn’t (thoroughly) check the the jitter (monophonic) activations to see if those performed better, which was definitely the case with the other acoustic sounds I tested the system with. There’s definitely something to be said for testing/trying it with acoustic sounds as the differentiation is much more subtle than when using the synthetic counterparts.
I’m attaching the dicts I created along with a shorter section of the “test beat” if anyone else wants to give it a spin.
trained dicts.zip (5.2 KB)
beat for testing.wav.zip (758.1 KB)