practical FFT

>>If you have a signal of 3.5 Hz, the peak will be at bins 3 and 4 in
>either
>>the 200 or 2000 point DFT.
>>
>>If you have two tones, say 3.2 Hz and 3.6 Hz as your signal, it won't
>look
>>much different superficially.
>>
>>However if you expand your interval by a factor of 10 (up to 10 seconds
>>now), whether you use 200 points or 2000 points, the two tones will be
>at
>>bins 32 and 36, whereas the 3.5 Hz tone would be at bin 35.  It would
be
>>easy to tell them apart.
>
>Dear Cedron & others,
>
>I am opening an old topic.
>
>I am not able to fully appreciate the last paragraph. I have worked out
a
>Matlab example and actually see the effect of increasing the increasing
>FFT interval. However, I am looking for intuitive explanation on how
>increasing the interval can change the bin values in the FFT
>
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I am wondering if the Matlab 1 based indexing is muddling this up for you.
 When I use the term "bin number" it is zero based.  So bin 0 is Matlab
array element 1, bin 1 is array element 2, and so on.

The bin number is the better scheme because the bin number corresponds to
the number of cycles per interval (also called frame) that will fill that
bin.

In the last paragraph, I was contrasting increasing the number of samples
by either increasing the sample rate vs. increasing the interval length,
and the effect this has on the ability to distinguish two nearby tones in
the same signal.

Once again, perhaps more concisely, if you increase the sampling rate you
are not increasing the number of cycles for a given tone within your
interval so the peak stays at the same bin number.  On the other hand, if
you increase your interval you also increase the number of cycles within
the interval proportionately and thus move the peak to a new bin number. 
If you use bin numbers instead of Matlab indexes, you should find the bin
number also increases by the same proportion.

Hope this helps.

Ced
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