I'm working on a project to recognize simple audio patterns. I have two data sets, each made up of between 4 and 32 note/duration pairs. One set is predefined, the other is from an incoming data stream. The length of the two strongly correlated data sets is often different, but roughly the same "shape". My goal is to come up with some sort of ranking as to how well the two data sets correlate/match.

I have converted the incoming frequencies to pitch and shifted the incoming data stream's pitch so that it's average pitch matches that of the predefined data set. I also stretch/compress the incoming data set's durations to match the overall duration of the predefined set. Here are two graphical examples of data that should be ranked as strongly correlated:

http://s2.postimage.org/FVeG0-ee3c23ecc094a55b15e538c3a0d83dd5.gif

(Sorry, as a new user I couldn't directly post images)

I'm doing this on a 8-bit microcontroller so resources are minimal. Speed is less an issue, a second or two of processing isn't a deal breaker.

It wouldn't surprise me if there is an obvious solution, I've just been staring at the problem too long. Any ideas?

Thanks in advance...