Hi Kyle,
Good call - that's exactly what I didn't spend much time on! I'll have to read up and experiment with that.
Thanks, p
On Sep 1, 2006, at 3:56 PM, Kyle Klipowicz wrote:
Paris~
I really would recommend the "learn" method, it creates a fft-based profile (I think using a neural net, correct me someone if I'm mistaken) of the sounds through a training routine of a suggested 10 occurrences.
I have not played with this feature too much myself, but imagine that if it works even halfway decent, it could do much to better your applications. Please keep the list updated on your success/failure with this feature.
~Kyle
On 9/1/06, Paris Treantafeles paris@parisgraphics.com wrote:
Hi Kyle,
Thanks - I should have mentioned that I use bonk~ in some patches as well. I've had better luck with it though when there's not too many different simultaneous frequencies going on. That's what got me filtering the input first- but I will take another look at it, maybe i just didn't use it's full possibilities.
-p
On Sep 1, 2006, at 3:41 PM, Kyle Klipowicz wrote:
Instead of filters, try using bonk~, which you can train tto differentiate various attacks and label them accordingly in its output.
~Kyle
On 9/1/06, Paris Treantafeles paris@parisgraphics.com wrote:
Hello List,
I have some Gem patches that react to incoming sound in various
ways.
Up until now, I would take the adc~ input and use 3 vcf~ 's
(one for
bass, mid and high) and with the right settings of Hz and Q, I was getting fairly good envelopes. Looking at this again and wanting to improve it, I have been experimenting with iemlib filters: http://pd.iem.at/iemlib/abstract.html
I was wondering if anyone can recommend which of these would be
best
for trying to isolate, say a bass drum or a hi hat from an incoming mix?
I read the Bessel filters are often used in cross overs but
from just
experimenting a bit it seems some of the others (e.g. vcf_lp2~)
are
giving me a better response.
Thanks, p
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