If I ave 2 otherwise identical 1D signals that are phase shifted from each other then cross-correlation is a perfect way to identify the time lag between the 2 signals.
Now supposing I double the speed of one of those signals (eg sampled at every other sample). The cross correlation will fail miserably at this point.
However, in image we can account for this kind of scaling (or even rotation) by converting the images to log polar coordinates and then performing the [phase] correlation. This has the added advantage of being able to identify the angle of rotation and amount of scaling, I believe.
Is there a similar, simple transform for 1D signals to allow for identification of the time lag as well as identifying the amount of time compression of the signal?
I guess you could use log polar coordinates directly on the 1D signal but it would, instantly, square the memory requirements. Any other thoughts?