Hi list,
I have a -batch patch that randomly samples the parameter space of control signals to (some black box)* and uses [sigmund~] to detect pitched-ness vs unpitchedness, keeping the pitch info and then a notch filter to detect distortion:
http://mathr.co.uk/softrockiii/2012-10-05_probe-puredata.png
So I end up with gnuplots that look something like this:
http://mathr.co.uk/softrockiii/2012-10-05_pitching-distortion.png
Now I want to find the best match for a given pitch and distortion and get back the corresponding parameters that approximately reconstruct the desired sound.
What's the best way to go about this?
I might end up just hand-rolling some Lua for this kind of matching if no one has any better ideas.
- currently mutually modulating FM oscillators like
http://archive.org/details/ClaudiusMaximus_-_Soft_Rock_EP concept to be resurrected and reworked/combined with concepts from NoIn EP http://gosub10.org/GOSUB10-002.html
Claude
Hi Claude,
On 5 Oct 2012, at 09:47, Claude Heiland-Allen claude@mathr.co.uk wrote:
Hi list,
I have a -batch patch that randomly samples the parameter space of control signals to (some black box)* and uses [sigmund~] to detect pitched-ness vs unpitchedness, keeping the pitch info and then a notch filter to detect distortion:
http://mathr.co.uk/softrockiii/2012-10-05_probe-puredata.png
So I end up with gnuplots that look something like this:
http://mathr.co.uk/softrockiii/2012-10-05_pitching-distortion.png
Now I want to find the best match for a given pitch and distortion and get back the corresponding parameters that approximately reconstruct the desired sound.
What's the best way to go about this?
I might end up just hand-rolling some Lua for this kind of matching if no one has any better ideas.
You may be interested in the [knn] external, which implements the k's nearest neighbour algorithm.
It can be found in svn in postlude/knn.
The classic paper on its use can be found here: http://www.music.mcgill.ca/~ich/research/icmc00/icmc00.timbre.pdf
best,
Jamie
Hi Claude,
On 5 Oct 2012, at 09:47, Claude Heiland-Allen claude@mathr.co.uk wrote:
Hi list,
I have a -batch patch that randomly samples the parameter space of control
signals to (some black box)* and uses [sigmund~] to detect pitched-ness vs unpitchedness, keeping the pitch info and then a notch filter to detect distortion:
http://mathr.co.uk/softrockiii/2012-10-05_probe-puredata.png
So I end up with gnuplots that look something like this:
http://mathr.co.uk/softrockiii/2012-10-05_pitching-distortion.png
Now I want to find the best match for a given pitch and distortion and get
back the corresponding parameters that approximately reconstruct the desired sound.
What's the best way to go about this?
I might end up just hand-rolling some Lua for this kind of matching if no
one has any better ideas.
You may be interested in the [knn] external, which implements the k's nearest neighbour algorithm.
It can be found in svn in postlude/knn.
The classic paper on its use can be found here: http://www.music.mcgill.ca/~ich/research/icmc00/icmc00.timbre.pdf
best,
Jamie
There may be a way to use the simile algorithm (from ekext) that gives the matched-ness of two numbers based on an arbitrary window size, but it still might involve rolling your own. I have a feeling KNN is very efficient... Ed
On 5 Oct 2012, at 10:20, Ed Kelly morph_2016@yahoo.co.uk wrote:
There may be a way to use the simile algorithm (from ekext) that gives the matched-ness of two numbers based on an arbitrary window size, but it still might involve rolling your own. I have a feeling KNN is very efficient... Ed
It's efficient, but there may still be room for optimisation in the implementation if that is a priority. It's also conceptually very simple, which is one reason I like it.
best,
Jamie
hi Claude, why you just dont calculate the distance between your values for calculating similarity? is that possible?
cheers
U.
2012/10/5 Jamie Bullock jamie@jamiebullock.com
On 5 Oct 2012, at 10:20, Ed Kelly morph_2016@yahoo.co.uk wrote:
There may be a way to use the simile algorithm (from ekext) that gives
the matched-ness of two numbers based on an arbitrary window size, but it still might involve rolling your own. I have a feeling KNN is very efficient...
Ed
It's efficient, but there may still be room for optimisation in the implementation if that is a priority. It's also conceptually very simple, which is one reason I like it.
best,
Jamie _______________________________________________ Pd-list@iem.at mailing list UNSUBSCRIBE and account-management -> http://lists.puredata.info/listinfo/pd-list
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Claude Heiland-Allen -
Ed Kelly -
Jamie Bullock -
umberto torrez