Hi folks,

I need to compute the autocorrelation of an input. The "naive" way takes way too long for Pd so I decided to do it via the Wiener-Khinchin Theorem, which solves this problem in N log N time.

In my understanding it's just: Input Stream -> FFT -> a²+b² -> IFFT.
But that doesn't work out. It outputs noise and not the smooth curve to be expected.

In the attachment is a patch where Wiener-Khinchin is applied to a Hann window. I know there are some people around who are into this matter. So, please show up and help me! I'd be grateful.

http://www.pdpatchrepo.info/hurleur/wienerkhinchin.pd

wow, thanks! especially to you, katja.

i don't know why, but i already knew the article about your helmholtz~ patch and somehow dismissed it, because i expected to find more useful information on the remainder of your website (which is great btw.!). although it (that very article) contains all the information i need. :D
and many thanks for the patch. it will certainly help me find inspiration. not too much though, since i am working on a term paper (university) and I am expected to do the work, not you

my plan is to write a pd-patch to measure the jitter (variance of period length) of an incoming signal on-line via comparing the length of individual periods. i hope, pd will be fast enough, otherwise i will have to reactivate my C skills. i'm gonna share my patch here when i'm done and successful, of course.

narfson