So basically, what I am attempting to do is design an algorithm that can take some amount of samples (say 10-20) that all have a common sound playing in them, but each with different noise (some with the same noise).
The noise isn't removable by normal processes (as the "noise" is actually the rest of a song), but I was wondering what process would be best for trying to isolate the original sound.
My original thought was, spectrally, to effectively take the minimum at a given frequency between two of the samples at a time, ideally leaving only the original sound. (i.e. the noise will not always be in the same place, meaning if it is zero in any of the samples, then it is not part of the original sound).
This sounds great, but due to my limited knowledge of FFTs I feel that I am underestimating or overlooking something significant (such as an FFT not being close enough to 100% accurate to the original sample for this to work).