https://www.sciencedirect.com/topics/engineering/matched-fil...
https://en.m.wikipedia.org/wiki/Matched_filter
I wonder if this technique has been tested in “noisy” signal environments? Or is this just useful in infinite SNR applications?
The case shown here should be more resilient to changes in signal amplitude and non-matching interference.
Of course, because the kernel is actually learned, it can adapt to interference and try to be a "matched filter to what we want, plus anti-matched filter to interference" (i.e. give near-0 response to common interference signals). But SCS does seem like a more natural way to achieve a similarity metric and feature detection. (although this non-flexibility might work against it)
of course noise is not gaussian in many interesting applications and nothing is stopping a nonlinear function from getting better results than a matched filter, we just have no theory to discover what such a function would be
so yes, like you, i'd need to see results on actual data before spending time to learn more about it...
https://github.com/stevenwalton/SCS-CCT
4% improvement over Compact Transformers, which is considered SOTA
EDIT: As meragrin_ pointed out, I read the graph the wrong way. It's actually 4% worse. Sorry.