A few overview questions about FFT. Relationships between input, buffer size, and expected output?
So, I'm not an expert at all, but I feel that I've perhaps learned enough to be somewhat helpful to anyone who ends up here with little introduction to FFT.  If any of this is wrong, see the "P.S" at the bottom.  tl;dr I'd rather share than leave this question unanswered entirely.

A few things are important to note: Parseval's theorem, bin frequencies, and normalization:

Parseval's theorem is something to the ends that the energy of the signal will be the energy of FFT bins (bins being each frequency/magnitude pair).  So, what this means in practice (I think) is that a small buffer size will make the bin magnitude of a sine wave frequency vary based off the amount of high amplitude wave is in the buffer.  It won't be consistent.

Bin Frequencies are the frequency of each bin from your FFT.  They aren't going to be the exact numbers you are looking for without a bit of work, but they should be close enough to give you some info.

Normalization is something that helps with all this.  For my project, I found the max bin magnitude, averaged it with the max from the last cycle, and then divided everything by it.  The result is that my visualization is showing something much more like what my ears feel they would see.

Now for the Q's (assuming all signals are normalized to -1 &lt; x &lt; 1):


Magnitudes of ~200 +-100, in the bins near the signal wave freq
Same, but magnitudes of ~300 +-50.  The larger buffer limits cases of more or less high amplitude chunking (sin(x) at [0 to Pi/4] vs [Pi/4 to Pi/2])
Magnitudes of ~150 +-100, in the bins near the signal wave freq.  The sweeping spreads out the bins a little, but the small buffer size (relative to sweep speed) helps.
Magnitudes of ~50 +-100, in the bins touched by the signal wave freq.  If the sweep is larger or faster expect the magnitudes to be even more spread out.  Note: the more bins touched, the lower the magnitudes overall.
Magnitudes of ~5 +-5, in all bins which happen to be strongly represented in this small buffer chunk
Magnitudes of ~1 +-1 in all bins.


So, it seems there is probably some form of log function which would help make the perceived levels of all these sounds not so distant when graphed by magnitude, but, overall, using normalization has worked in my project to pick out the most noteworthy note tones.  (btw notes and tones are anagrams)

P.S.  If any of this is wrong, please do make an answer covering and correcting these topics.  I will not be marking this as the accepted answer for at least a month because I'm not experienced enough to be an authority on this subject.  That being said, this is what I think may be true based off the results I've been getting, and it's better than nothing for a newcommer.