Template matching is normally doing the pixelwise correlation between source + offset and dest (in 2D). Its a really basic algorithm, even more basic in 1D. So

Template matching is normally doing the pixelwise correlation between source + offset and dest (in 2D). Its a really basic algorithm, even more basic in 1D. So it would be pretty trivial to just compare the shifted spectrograph profiles for a massive gain in performance. Infact, you probably don't even the "shift" bit of the algorithm because you will end up comparing one frequency to a different frequency which does not make much sense (outside of Doppler shift calcs). So its a really long winded approach to taking the cross correlation of two spectrograms that introduces a load of unwanted homomorphism. see also http://dsp.stackexchange.com/questions/736/how-do-i-implement-cross-correlation-to-prove-two-audio-files-are-similar http://dsp.stackexchange.com/questions/736/how-do-i-implemen... I would like to add that just because an algorithm is in vision processing library, doesn't make it "clever". Basic template matching implementation is 4 nested for loops. Clever template matching is FFT to save one two of those loops, but the results are the same. In audio processing you don't need all those loops because the signal is 1D. So going via a vision processing library is just introducing pointless loops that do nothing but worsen results and slow down code.