Is `xcov()`More Accurate Than `xcorr()` to Compute the Cross Correlation of Signals with Non Zero Mean Values?
When trying to measure similarity between signals we're basically building a metric.
When doing so we need to ask what we want to be sensitive about.
For instance, if you don't remove the DC Component (The Mean) and use something like an integral to measure similarity (Correlation / Convolution based) then you are sensitive the added DC component.
For instance, take 2 random white noises and add to each a DC component of 1e6.
Measure their similarity by correlation and compare it to 2 Sine Waves with amplitude of 1e-3 and a relative shift phase of 1e-6 [Rad].
In your eyes, which should be more similar? Which is indeed?
I'd say that in most cases in signal processing, similarity should be measured ignoring the DC component.