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Windows

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Why Use Windows? Available Window Functions Graphical User Interface Tools Basic Shapes Generalized Cosine Windows Kaiser Window Chebyshev Window

Why Use Windows?

In both digital filter design and spectral estimation, the choice of a windowing function can play an important role in determining the quality of overall results. The main role of the window is to damp out the effects of the Gibbs phenomenon that results from truncation of an infinite series.

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Available Window Functions

Window	Function
Bartlett-Hann window	barthannwin
Bartlett window	bartlett
Blackman window	blackman
Blackman-Harris window	blackmanharris
Bohman window	bohmanwin
Chebyshev window	chebwin
Flat Top window	flattopwin
Gaussian window	gausswin
Hamming window	hamming
Hann window	hann
Kaiser window	kaiser
Nuttall's Blackman-Harris window	nuttallwin
Parzen (de la Valle-Poussin) window	parzenwin
Rectangular window	rectwin
Tapered cosine window	tukeywin
Triangular window	triang

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Graphical User Interface Tools

Two graphical user interface tools are provided for working with windows in the Signal Processing Toolbox product:

• Window Design and Analysis Tool (wintool)

• Window Visualization Tool (wvtool)

Refer to the reference pages for these tools for detailed information.

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Basic Shapes

The basic window is the rectangular window, a vector of ones of the appropriate length. A rectangular window of length 50 is

n = 50;
w = rectwin(n);

This toolbox stores windows in column vectors by convention, so an equivalent expression is

w = ones(50,1);

To use the Window Design and Analysis Tool to create this window, type

wintool

wintool opens with a default Hamming window. In the Current Window Information panel, set Type = Rectangular and Length = 50 and then press Apply.

The Bartlett (or triangular) window is the convolution of two rectangular windows. The functions bartlett and triang compute similar triangular windows, with three important differences. The bartlett function always returns a window with two zeros on the ends of the sequence, so that for n odd, the center section of bartlett(n+2) is equivalent to triang(n):

bartlett(7)
ans =
         0
    0.3333
    0.6667
    1.0000
    0.6667
    0.3333
         0
triang(5)
ans =
    0.3333
    0.6667
    1.0000
    0.6667
    0.3333

For n even, bartlett is still the convolution of two rectangular sequences. There is no standard definition for the triangular window for n even; the slopes of the line segments of the triang result are slightly steeper than those of bartlett in this case:

w = bartlett(8); 
[w(2:7)  triang(6)]
ans =
    0.2857    0.1667
    0.5714    0.5000
    0.8571    0.8333
    0.8571    0.8333
    0.5714    0.5000
    0.2857    0.1667

You can see the difference between odd and even Bartlett windows in WinTool.

The final difference between the Bartlett and triangular windows is evident in the Fourier transforms of these functions. The Fourier transform of a Bartlett window is negative for n even. The Fourier transform of a triangular window, however, is always nonnegative.

The following figure, which is a zoomed version of the Frequency domain plot of 8-point Bartlett and Triangular windows with the y-axis set to Zerophase, illustrates this difference.

This difference can be important when choosing a window for some spectral estimation techniques, such as the Blackman-Tukey method. Blackman-Tukey forms the spectral estimate by calculating the Fourier transform of the autocorrelation sequence. The resulting estimate might be negative at some frequencies if the window's Fourier transform is negative (see Kay [1], pg. 80).

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Generalized Cosine Windows

Blackman, Flat Top, Hamming, Hann, and rectangular windows are all special cases of the generalized cosine window. These windows are combinations of sinusoidal sequences with frequencies 0, , and , where N is the window length. One way to generate them is

ind = (0:n-1)'*2*pi/(n-1);
w = A - B*cos(ind) + C*cos(2*ind);

where A, B, and C are constants you define. The concept behind these windows is that by summing the individual terms to form the window, the low frequency peaks in the frequency domain combine in such a way as to decrease sidelobe height. This has the side effect of increasing the mainlobe width.

The Hamming and Hann windows are two-term generalized cosine windows, given by A = 0.54, B = 0.46 for Hamming and A = 0.5, B = 0.5 for Hann (C = 0 in both cases). The hamming and hann functions, respectively, compute these windows.

Note that the definition of the generalized cosine window shown in the earlier MATLAB code yields zeros at samples 1 and n for A = 0.5 and B = 0.5.

The Blackman window is a popular three-term window, given by A = 0.42, B = 0.5, C = 0.08. The blackman function computes this window.

The Flat Top window is a five-term window and is used for calibration. It is given by A = 1, B = 1.93, C = 1.29, D =0.388, and E = 0.322.

This WinTool compares Blackman, Hamming, Hann, and Flat Top windows.

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Kaiser Window

The Kaiser window is an approximation to the prolate-spheroidal window, for which the ratio of the mainlobe energy to the sidelobe energy is maximized. For a Kaiser window of a particular length, the parameter β controls the sidelobe height. For a given β, the sidelobe height is fixed with respect to window length. The statement kaiser(n,beta) computes a length n Kaiser window with parameter beta.

Examples of Kaiser windows with length 50 and beta parameters of 1, 4, and 9 are shown in this wintool example.

To create these Kaiser windows using the MATLAB command line,

n = 50;
w1 = kaiser(n,1);
w2 = kaiser(n,4);
w3 = kaiser(n,9);
[W1,f] = freqz(w1/sum(w1),1,512,2);
[W2,f] = freqz(w2/sum(w2),1,512,2);
[W3,f] = freqz(w3/sum(w3),1,512,2);
plot(f,20*log10(abs([W1 W2 W3]))); grid;
legend('beta = 1','beta = 4','beta = 9',3)

As β increases, the sidelobe height decreases and the mainlobe width increases. This wintool shows how the sidelobe height stays the same for a fixed β parameter as the length is varied.

To create these Kaiser windows using the MATLAB command line:

w1 = kaiser(50,4);
w2 = kaiser(20,4);
w3 = kaiser(101,4);
[W1,f] = freqz(w1/sum(w1),1,512,2);
[W2,f] = freqz(w2/sum(w2),1,512,2);
[W3,f] = freqz(w3/sum(w3),1,512,2);
plot(f,20*log10(abs([W1 W2 W3]))); grid;
legend('length = 50','length = 20','length = 101')

Kaiser Windows in FIR Design

There are two design formulas that can help you design FIR filters to meet a set of filter specifications using a Kaiser window. To achieve a sidelobe height of -α dB, the beta parameter is

For a transition width of Δω rad/s, use the length

Filters designed using these heuristics will meet the specifications approximately, but you should verify this. To design a lowpass filter with cutoff frequency  rad/s, transition width  rad/s, and 40 dB of attenuation in the stopband, try

[n,wn,beta] = kaiserord([0.4 0.6]*pi,[1 0],[0.01 0.01],2*pi);
h = fir1(n,wn,kaiser(n+1,beta),'noscale');

The kaiserord function estimates the filter order, cutoff frequency, and Kaiser window beta parameter needed to meet a given set of frequency domain specifications.

The ripple in the passband is roughly the same as the ripple in the stopband. As you can see from the frequency response, this filter nearly meets the specifications:

fvtool(h,1);

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Chebyshev Window

The Chebyshev window minimizes the mainlobe width, given a particular sidelobe height. It is characterized by an equiripple behavior, that is, its sidelobes all have the same height.

As shown in the Time Domain plot, the Chebyshev window has large spikes at its outer samples.

For a detailed discussion of the characteristics and applications of the various window types, see Oppenheim and Schafer [3], pgs. 444-462, and Parks and Burrus [4], pgs. 71-73.

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Special Topics

Parametric Modeling

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