# Reduced frequency range FFT

Generally when one takes the FFT of a signal it "works" over the whole bandwidth dividing up the spectrum into chunks given by the resolution. If the bandwidth of the signal is 10khz and your resolution is 1000 then each "frequency" represents a chunk of 10hz(each bin is 10hz in size).

The problem with this method is that it gives the same "size" to each bin even though lower frequencies loose resolution. e.g., a 10hz bin around 25hz frequency contains much more information than 10hz at 8kz. This issue is not hard to fix with the analytical FT since it is just a matter of scale. Is it possible adapt the FFT to have a frequency dependent bin-size?

Essentially when we divide up the frequency range into n chunks we want lower frequencies to have more accuracy since they are lower frequencies.

e.g., I might want a resolution of 0.1hz in the lower frequencies and 10hz in the higher frequencies. The problem with the current FFT is that we must use the highest resolution overall. That is, Because I want a 0.1hz resolution in the lower frequencies I MUST have an 0.1hz resolution in the higher resolutions. This means I'll require a much higher n-point transform than I really need.

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Have you looked at wavelets? (By the way, I think your description of chunks or bins of the frequency range being represented by individual frequencies in the Fourier analysis is misleading.) –  joriki Jul 22 '12 at 15:29
@joriki There is nothing misleading about using the term bins. It is very similar to that of a histogram. I believe you are confused on terminology/understanding my question rather than the term being misleading. –  AbstractDissonance Jul 22 '12 at 19:46
I didn't say that the term "bins" is misleading. I believe your entire description of chunks or bins of frequency range being represented by individual frequencies is misleading. The frequencies in the discrete Fourier analysis stand only for themselves; they don't "represent" any nearby frequencies. –  joriki Jul 22 '12 at 19:57
@joriki: I'm sorry but your understanding of the DFT is a bit skewed. The DFT transforms a sequence of numbers to another sequence of numbers BUT when you interpret those numbers to physical meaning then do represent a "wash" or a "bin" of frequencies. There are many reasons for this and in the real world devices that convert signals to the digital domain are not even accurate enough and so each data point represents a "bin" around some point. In any case your only correct if we have infinite accuracy which we don't and the reason why I would like a low-frequency weighted transform. –  AbstractDissonance Jul 22 '12 at 20:14
I don't think you're use of "correct" is appropriate here. I deliberately didn't say that you were incorrect, since this talk of bins and chunks being represented isn't formal enough to be correct or incorrect. I said that it's misleading, and I said so in the context of a site on mathematics. You may well be right that it has its merits in the context of inaccurate measurements, but I do believe that for someone trying to understand the discrete Fourier transform mathematically it can be misleading. –  joriki Jul 22 '12 at 20:23