Hi! I would like to speed up a cross correlation application which looks like this: x has to be cross-correlated with "n" different length y's. I would like to precompute the FFT of the x before making the convolution with all the y's. Thats why I dont want to use the classic convolution and cross correlation functions available in Matlab. (nor the filtering functions). Any suggestions? Thanks! Atmapuri

# Fast cross correlation!

Started by ●October 7, 2004

Reply by ●October 8, 20042004-10-08

"Atmapuri" <janez.makovsek@usa.net> wrote in message news:<KTb9d.3912$F6.1073538@news.siol.net>...> Hi! > > I would like to speed up a cross correlation > application which looks like this: > > x has to be cross-correlated with "n" different > length y's. > > I would like to precompute the FFT of the x > before making the convolution with all the y's. > Thats why I dont want to use the classic > convolution and cross correlation functions > available in Matlab. (nor the filtering functions). > > Any suggestions?You are using matlab, right? Try this (I haven't tested it, so no guarantees about the details): Y= [y1,y2,y3,...,yn]; % All the ys are stored as colomns in a matrix. % If the ys are of different length, append zeros. M=length(Y(:,1)); % Length of columns in matrix Y N= length(x); % Length of data, which is stored as column vector L= max(M,N); % MAke data matrix from x vector X=x*ones(1,length(Y(1,:))); % Compute DFT of columns of X and Y. Zero-pad the shorter sequence % to match the length of the longer one, and correct for wrap-around % effects. The details can be a bit more elegant... XX=fft(X,1,2*L); YY=fft(Y,1,2*L); SXY= XX.*conj(YY); rsy=fftshift(real(ifft(SXY,1))); The one thing that remains is to find the correct time axes. Rune

Reply by ●October 9, 20042004-10-09

Hi! Thanks. Would you have any references to why you have to XX=fft(X,1,2*L); double length of FFT with zero padding? Thanks! Atmapuri "Rune Allnor" <allnor@tele.ntnu.no> wrote in message news:f56893ae.0410080132.71d4362d@posting.google.com...> "Atmapuri" <janez.makovsek@usa.net> wrote in messagenews:<KTb9d.3912$F6.1073538@news.siol.net>...> > Hi! > > > > I would like to speed up a cross correlation > > application which looks like this: > > > > x has to be cross-correlated with "n" different > > length y's. > > > > I would like to precompute the FFT of the x > > before making the convolution with all the y's. > > Thats why I dont want to use the classic > > convolution and cross correlation functions > > available in Matlab. (nor the filtering functions). > > > > Any suggestions? > > You are using matlab, right? Try this (I haven't tested it, so no > guarantees about the details): > > Y= [y1,y2,y3,...,yn]; % All the ys are stored as colomns in a matrix. > % If the ys are of different length, append zeros. > > M=length(Y(:,1)); % Length of columns in matrix Y > > N= length(x); % Length of data, which is stored as column vector > > L= max(M,N); > > % MAke data matrix from x vector > X=x*ones(1,length(Y(1,:))); > > % Compute DFT of columns of X and Y. Zero-pad the shorter sequence > % to match the length of the longer one, and correct for wrap-around > % effects. The details can be a bit more elegant... > > XX=fft(X,1,2*L); > YY=fft(Y,1,2*L); > > SXY= XX.*conj(YY); > rsy=fftshift(real(ifft(SXY,1))); > > The one thing that remains is to find the correct time axes. > > Rune

Reply by ●October 10, 20042004-10-10