Matlab/Octave fftshift utility

Matlab and Octave have a simple utility called fftshift that performs this bin rotation. Consider the following example:

octave:4>
fftshift([1 2 3 4])
ans =
  3  4  1  2
octave:5>

If the vector [1 2 3 4] is the output of a length 4 FFT, then the first element (1) is the dc term, and the third element (3) is the point at half the sampling rate (), which can be taken to be either plus or minus since they are the same point on the unit circle in the plane. Elements 2 and 4 are plus and minus , respectively. After fftshift, element (3) is first, which indicates that both Matlab and Octave regard the spectral sample at half the sampling rate as a negative frequency. The next element is 4, corresponding to frequency , followed by dc and .

Another reasonable result would be fftshift([1 2 3 4]) == [4 1 2 3], which defines half the sampling rate as a positive frequency. However, giving to the negative frequencies balances giving dc to the positive frequencies, and the number of samples on both sides is then the same. For an odd-length DFT, there is no point at , so the result

octave:4>
fftshift([1 2 3])
ans =
  3  1  2
octave:5>

is the only reasonable answer, corresponding to frequencies , respectively.

Index Ranges for Zero-Phase Zero-Padding.

Having looked at zero-phase zero-padding ``pictorially'' in matlab buffers, let's now specify the index-ranges mathematically. Denote the window length by (an odd integer) and the FFT length by (a power of 2). Then the windowed data will occupy indices 0 to (positive-time segment), and to (negative-time segment). Here we are assuming a 0-based indexing scheme as used in C or C++. We add 1 to all indices for matlab indexing to obtain 1:(M-1)/2+1 and N-(M-1)/2+1:N, respectively. The zero-padding zeros go in between these ranges, i.e., from to .

Summary.

To summarize, zero-padding is used for

• padding out to the next higher power of 2 so the FFT can be used with any window length,
• improving the quality of spectral displays, and
• oversampling spectral peaks so that some simple final interpolation will be accurate.

In addition, we will learn in Chapter 7 that zero-padding is also necessary to accommodate spectral modifications in the short-time Fourier transform (STFT). This is because spectral modifications cause the time-domain signal to lengthen in time, and without sufficient zero-padding to accommodate it, there will be time aliasing in the reconstruction of the signal from the modified FFTs.

Some examples of interpolated spectral display by means of zero-padding may be seen in §3.4.

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About the Author: Julius Orion Smith III
Julius Smith's background is in electrical engineering (BS Rice 1975, PhD Stanford 1983). He is presently Professor of Music and Associate Professor (by courtesy) of Electrical Engineering at Stanford's Center for Computer Research in Music and Acoustics (CCRMA), teaching courses and pursuing research related to signal processing applied to music and audio systems. See http://ccrma.stanford.edu/~jos/ for details.