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 }