The need for spectral interpolation comes up in many situations. For example, we always use the DFT in practice, while conceptually we often prefer the DTFT. For time-limited signals, that is, signals which are zero outside some finite range, the DTFT can be computed from the DFT via spectral interpolation. Conversely, the DTFT of a time-limited signal can be sampled to obtain its DFT.3.7Another application of DFT interpolation is spectral peak estimation, which we take up in Chapter 5; in this situation, we start with a sampled spectral peak from a DFT, and we use interpolation to estimate the frequency of the peak more accurately than what we get by rounding to the nearest DFT bin frequency.
The following sections describe the theoretical and practical details of ideal spectral interpolation.
Thus, for signals in the DTFT domain which are time limited to , we obtain
This can be thought of as a zero-centered DFT evaluated at instead of for some . It arises naturally from taking the DTFT of a finite-length signal. Such time-limited signals may be said to have ``finite support'' .
Interpolating a DFT
(The aliased sinc function, , is derived in §3.1.) Thus, zero-padding in the time domain interpolates a spectrum consisting of samples around the unit circle by means of `` interpolation.'' This is ideal, time-limited interpolation in the frequency domain using the aliased sinc function as an interpolation kernel. We can almost rewrite the last line above as , but such an expression would normally be defined only for , where is some integer, since is discrete while is continuous.
Figure F.1 lists a matlab function for performing ideal spectral interpolation directly in the frequency domain. Such an approach is normally only used when non-uniform sampling of the frequency axis is needed. For uniform spectral upsampling, it is more typical to take an inverse FFT, zero pad, then a longer FFT, as discussed further in the next section.
Zero Padding in the Time Domain
Unlike time-domain interpolation , ideal spectral interpolation is very easy to implement in practice by means of zero padding in the time domain. That is,
Since the frequency axis (the unit circle in the plane) is finite in length, ideal interpolation can be implemented exactly to within numerical round-off error. This is quite different from ideal (band-limited) time-domain interpolation, in which the interpolation kernel is sinc ; the sinc function extends to plus and minus infinity in time, so it can never be implemented exactly in practice.3.9
To interpolate a uniformly sampled spectrum , by the factor , we may take the length inverse DFT, append zeros to the time-domain data, and take a length DFT. If is a power of two, then so is and we can use a Cooley-Tukey FFT for both steps (which is very fast):
This operation creates new bins between each pair of original bins in , thus increasing the number of spectral samples around the unit circle from to . An example for is shown in Fig.2.4 (compare to Fig.2.3).
X = fft(x,N); % FFT size N > length(x)
Another reason we zero-pad is to be able to use a Cooley-Tukey FFT with any window length . When is not a power of , we append enough zeros to make the FFT size be a power of . In Matlab and Octave, the function nextpow2 returns the next higher power of 2 greater than or equal to its argument:
N = 2^nextpow2(M); % smallest M-compatible FFT size
Suppose we perform spectrum analysis on some sinusoid using a length window. Without zero padding, the DFT length is . We may regard the DFT as a critically sampled DTFT (sampled in frequency). Since the bin separation in a length- DFT is , and the zero-crossing interval for Blackman-Harris side lobes is , we see that there is one bin per side lobe in the sampled window transform. These spectral samples are illustrated for a Hamming window transform in Fig.2.3b. Since in Table 5.2, the main lobe is 4 samples wide when critically sampled. The side lobes are one sample wide, and the samples happen to hit near some of the side-lobe zero-crossings, which could be misleading to the untrained eye if only the samples were shown. (Note that the plot is clipped at -60 dB.)
If we now zero pad the Hamming-window by a factor of 2 (append 21 zeros to the length window and take an point DFT), we obtain the result shown in Fig.2.4. In this case, the main lobe is 8 samples wide, and there are two samples per side lobe. This is significantly better for display even though there is no new information in the spectrum relative to Fig.18.104.22.168
Incidentally, the solid lines in Fig.2.3b and 2.4b indicating the ``true'' DTFT were computed using a zero-padding factor of , and they were virtually indistinguishable visually from . ( is not enough.)
The examples in Fig.2.5 show how zero-padding helps in clarifying the true peak of the sampled window transform. With enough zero-padding, even very simple interpolation methods, such as quadratic polynomial interpolation, will give accurate peak estimates.
The previous zero-padding example used the causal Hamming window, and the appended zeros all went to the right of the window in the FFT input buffer (see Fig.2.4a). When using zero-phase FFT windows (usually the best choice), the zero-padding goes in the middle of the FFT buffer, as we now illustrate.
Figure 2.6a shows a windowed segment of some sinusoidal data, with the window also shown as an envelope. Figure 2.6b shows the same data loaded into an FFT input buffer with a factor of 2 zero-phase zero padding. Note that all time is ``modulo '' for a length FFT. As a result, negative times map to in the FFT input buffer.
Figure 2.7a shows the result of performing an FFT on the data of Fig.2.6b. Since frequency indices are also modulo , the negative-frequency bins appear in the right half of the buffer. Figure 2.6b shows the same data ``rotated'' so that bin number is in order of physical frequency from to . If is the bin number, then the frequency in Hz is given by , where denotes the sampling rate and is the FFT size.
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.
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 .
To summarize, zero-padding is used for
- padding out to the next higher power of 2 so a Cooley-Tukey 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.
Some examples of interpolated spectral display by means of zero-padding may be seen in §3.4.
The Rectangular Window
Continuous-Time Fourier Theorems