On Mon, 22 Aug 2005 19:16:11 -0500, "Tanriover" <c.tanriover@ieee.org>
wrote:

>I am trying to understand the difference between the sliding window FFT
>algorithm and the windowed FFT (i.e. the conventional method).
>
>Say we have a 6000 sample time domain data that we wish analyze in
>frequency. We can take two approaches:
>
>1- Pad the data stream with 2192 zeros and apply a 8192 point FFT.
>
>2- Choose a short window size (e.g 256 points) and slide this window
>across the 6000 data samples while taking the 256 point FFT and shifting
>the window by one sample after doing so. Continue this operation until the
>end of the 6000th sample.
>
>I realize that method 2 gives a short term frequency response of the data
>sample. However, I wonder if either of the above two methods are preferred
>over the other in certain cases.
>
>Also, in 2, after each FFT operation does one discard half of the spectrum
>and scale the amplitudes by the FFT window length as in conventional FFT?
>Are there any other issues that I should be aware of while applying the
>sliding window FFT algorithm?

Rune covered the major bases, so I'll just add a little bit.

The main application for the sliding DFT is when a spectrogram or
spectrogram-like output is desired, i.e., successive computations of
the DFT of the input in a sliding-window sense.   In this case the
sliding-window-DFT provides a substantial reduction in the required
computational load by making the process recursive and re-using the
previous output.   Recomputing the full DFT or FFT conventionally
would provide the exact same result, it would just require more
computation.

In the cases where the DFTof the previous window isn't available, then
there isn't much use for the sliding window algorithm.


Eric Jacobsen
Minister of Algorithms, Intel Corp.
My opinions may not be Intel's opinions.
http://www.ericjacobsen.org

Tanriover skrev:
> Hi,
>
> I am trying to understand the difference between the sliding window FFT
> algorithm and the windowed FFT (i.e. the conventional method).
>
> Say we have a 6000 sample time domain data that we wish analyze in
> frequency. We can take two approaches:
>
> 1- Pad the data stream with 2192 zeros and apply a 8192 point FFT.

If you have an elaborate FFT algorithm, you don't need to zero pad.

> 2- Choose a short window size (e.g 256 points) and slide this window
> across the 6000 data samples while taking the 256 point FFT and shifting
> the window by one sample after doing so. Continue this operation until the
> end of the 6000th sample.
>
> I realize that method 2 gives a short term frequency response of the data
> sample. However, I wonder if either of the above two methods are preferred
> over the other in certain cases.

The answer is yes.

The methods you describe are two different tools that are used for
different things. The 8192pt FFT provides one spectrum, represented
as a 1D vector,  that describes the whole data segment.

The result from applying the sliding window FFT you describe, is a
2D image of dimension frequency vs time, that sketches the spectrum
for different time intervals. This image is known as a spectrogram.

> Also, in 2, after each FFT operation does one discard half of the spectrum

If one uses the spectrogram to analyze real-valued data, it
is usually convenient to skip half the image in order to save
storage space.

If one works with complex-valued data, one can in general not
skip half the spectrum.

> and scale the amplitudes by the FFT window length as in conventional FFT?

Formally, yes, but not always in practice. Some implementations
of the FFT skips a normalization step to save some floating point
operations. The normalization step is not crucial in many
applications.

If you work with calibrated data sensors and want properly
quantified spectra, you need the proper scaling. You as user are
responsible for implementing the scaling when appropriate.

> Are there any other issues that I should be aware of while applying the
> sliding window FFT algorithm?

It is always useful to be aware of the Heisenberg uncertainty
principle. It states that you trade frequency resolution for
time localization, and vice versa.

Apart from that, just get yourself some simple data set (use
your computers microphone and sound card to record some sound)
and start analyzing. Getting experience with the tools is quite
useful.

Rune

Hi,

I am trying to understand the difference between the sliding window FFT
algorithm and the windowed FFT (i.e. the conventional method).

Say we have a 6000 sample time domain data that we wish analyze in
frequency. We can take two approaches:

1- Pad the data stream with 2192 zeros and apply a 8192 point FFT.

2- Choose a short window size (e.g 256 points) and slide this window
across the 6000 data samples while taking the 256 point FFT and shifting
the window by one sample after doing so. Continue this operation until the
end of the 6000th sample.

I realize that method 2 gives a short term frequency response of the data
sample. However, I wonder if either of the above two methods are preferred
over the other in certain cases.

Also, in 2, after each FFT operation does one discard half of the spectrum
and scale the amplitudes by the FFT window length as in conventional FFT?
Are there any other issues that I should be aware of while applying the
sliding window FFT algorithm?


Regards,
Cagri 






 
		
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