[chuck-users] Convolution FFT Performance
Mitch Kaufman
mitch.kaufman at hotmail.com
Mon Sep 21 22:49:08 EDT 2015
Hi List,
I was wondering if someone might be able to help with a performance issue I am having with ChucK in respect to the use of FFT/IFFT.
I've written a Pulsar Generator and I've added a convolution function to the pulse train of the generator. I found a previous thread that I've used as a basis for the convolution (see the posting below). The main change was to replace the following line:
adc => Gain input => FFT fftx => blackhole;
with this one:
SinOsc sin => WinFuncEnv env => Pan2 pan => Gain input => FFT fftx => blackhole;
At rest, the JACK DSP Load is about 2.2%. When I run the Pulsar Generator at a reasonable rate, the DSP Load jumps to about 3.5% or so without convolution. When I add the convolution function as below, it quickly jumps to a 100% usage with steady Xruns. The sample sizes I am using for the SndBuf statement below are somewhere between 18000 and 28000 samples.
Is there a strategy anyone could suggest to achieve greater performance of the convolution function below or are there alternative strategies for implementing convolution in general? I'd appreciate any advice.
+++
[chuck-users] Daniel: Convolution (FFT version)
Perry R Cook
prc at CS.Princeton.EDU
Tue Nov 25 19:09:31 EST 2014
Previous message: [chuck-users] Daniel: Convolution
Next message: [chuck-users] chugins won't compile
Messages sorted by:
[ date ]
[ thread ]
[ subject ]
[ author ]
FFT version.
Most efficient, lots of delay. Could chunk up
and factor, overlap-add for less delay. This is
the basic idea tho.
// FFT convolution with static impulse response
// by Perry R. Cook, November 2014
// upsides: as efficient as it could be, save for
// constructing a specific fft convolution chugin
// downsides: minimum delay is length of impulse response + buffers
// fix: break into pieces and overlap add
// Other fix: see filter version using my FIR Filter chugin
// our fixed convolution kernal (impulse response)
SndBuf s => FFT ffth => blackhole;
"CelloBodyShort.wav" => s.read; // whatever you like (caution of length!!)
2 => int fftSize;
while (fftSize < s.samples())
2 *=> fftSize; // next highest power of two
fftSize => int windowSize; // this is windowsize, only apply to signal blocks
windowSize/2 => int hopSize; // this can any whole fraction of windowsize
2 *=> fftSize; // zero pad by 2x factor (for convolve)
// our input signal, replace adc with anything you like
adc => Gain input => FFT fftx => blackhole; // input signal
IFFT outy => dac; // our output
fftSize => ffth.size => fftx.size => outy.size; // sizes
Windowing.hann(windowSize) => fftx.window;
// <<< s.samples(), fftSize >>>;
windowSize::samp => now; // load impulse response into h
ffth.upchuck() @=> UAnaBlob H; // spectrum of fixed impulse response
s =< ffth =< blackhole; // don't need impulse resp signal anymore
complex Z[fftSize/2];
1000 => input.gain; // fiddle with this how you like/need
while (true) {
fftx.upchuck() @=> UAnaBlob X; // spectrum of input signal
// multiply spectra bin by bin (complex for free!):
for(0 => int i; i < fftSize/2; i++ ) {
fftx.cval(i) * H.cval(i) => Z[i];
}
outy.transform( Z ); // take ifft
hopSize :: samp => now; // and do it all again
}
Regards,
Mitch
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.cs.princeton.edu/pipermail/chuck-users/attachments/20150921/d1b943e0/attachment.html>
More information about the chuck-users
mailing list