We currently have C/C++ FFT code with Hann Window function support. For a large data (over 10,000) we want to split this into 1024 samples with 50% overlap. For each 1024 sample, we window and fft.

What is the recommended way to combine all the results from these smaller overlapping results to obtain a presentable results for the over 10,000 data?

Update: For the poor explanation. The process is best illustrated by the following links



  • $\begingroup$ i wasn't the one to down-arrow you, but could you be more specific about what is is that you have done or can do, and then what it is that you're missing? maybe a little code or pseudo-code would be illustrative. $\endgroup$ Commented Aug 2, 2015 at 19:43
  • $\begingroup$ Why the overlap? Is the signal you expect a transient one? Overlap is usually done for better IFFT. $\endgroup$
    – Moti
    Commented Aug 3, 2015 at 3:52
  • $\begingroup$ I have added links to illustrate the process. My thought is to combine the non-overlapping FFT output for the final result, but could not find any source of information to confirm that approach. $\endgroup$
    – paulusnet
    Commented Aug 3, 2015 at 12:51

1 Answer 1


I'm not sure what your intent is, but the organization of the computation can be done using straightforward overlap-and-add or overlap-and-save methods.

If you're attempting to look at data statistics in the frequency domain, a good starting might be to look at Welch's method and its descendents.

  • $\begingroup$ Thanks, I found out it falls under FFT Averaging and looking into that right now. $\endgroup$
    – paulusnet
    Commented Aug 7, 2015 at 22:51

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