### Bandpass Filter Design Example

The matlab code below designs a bandpass filter which passes frequencies between 4 kHz and 6 kHz, allowing transition bands from 3-4 kHz and 6-8 kHz (*i.e.*, the stop-bands are 0-3 kHz and 8-10 kHz, when the sampling rate is 20 kHz). The desired stop-band attenuation is 80 dB, and the pass-band ripple is required to be no greater than 0.1 dB. For these specifications, the function

`kaiserord`returns a beta value of and a window length of . These values are passed to the function

`kaiser`which computes the window function itself. The ideal bandpass-filter impulse response is computed in

`fir1`, and the supplied Kaiser window is applied to shorten it to length .

fs = 20000; % sampling rate F = [3000 4000 6000 8000]; % band limits A = [0 1 0]; % band type: 0='stop', 1='pass' dev = [0.0001 10^(0.1/20)-1 0.0001]; % ripple/attenuation spec [M,Wn,beta,typ] = kaiserord(F,A,dev,fs); % window parameters b = fir1(M,Wn,typ,kaiser(M+1,beta),'noscale'); % filter designNote the conciseness of the matlab code thanks to the use of

`kaiserord`and

`fir1`from Octave or the Matlab Signal Processing Toolbox. Figure 4.6 shows the magnitude frequency response of the resulting FIR filter . Note that the upper pass-band edge has been moved to 6500 Hz instead of 6000 Hz, and the stop-band begins at 7500 Hz instead of 8000 Hz as requested. While this may look like a bug at first, it's actually a perfectly fine solution. As discussed earlier (§4.5), all transition-widths in filters designed by the window method must equal the window-transform's main-lobe width. Therefore, the only way to achieve specs when there are multiple transition regions specified is to set the main-lobe width to the

*minimum*transition width. For the others, it makes sense to

*center*the transition within the requested transition region.

####
Under the Hood of `kaiserord`

Without `kaiserord`, we would need to implement Kaiser's formula [115,67] for estimating the Kaiser-window required to achieve the given filter specs:

where is the desired stop-band attenuation in dB (typical values in audio work are to ). Note that this estimate for becomes too small when the filter pass-band width approaches zero. In the limit of a zero-width pass-band, the frequency response becomes that of the Kaiser window transform itself. A non-zero pass-band width acts as a ``moving average'' lowpass filter on the side-lobes of the window transform, which brings them down in level. The

`kaiserord`estimate assumes some of this side-lobe smoothing is present. A similar function from [198] for

*window design*(as opposed to filter design

^{5.7}) is

where now is the desired

*side-lobe attenuation*in dB (as opposed to stop-band attenuation). A plot showing Kaiser window side-lobe level for various values of is given in Fig.3.28. Similarly, the filter order is estimated from stop-band attenuation and desired transition width using the empirical formula

(5.13) |

where is in radians between

**0**and . Without the function

`fir1`, we would have to manually implement the window method of filter design by (1) constructing the impulse response of the ideal bandpass filter (a cosine modulated sinc function), (2) computing the Kaiser window using the estimated length and from above, then finally (3) windowing the ideal impulse response with the Kaiser window to obtain the FIR filter coefficients . A manual design of this nature will be illustrated in the Hilbert transform example of §4.6.

#### Comparison to the Optimal Chebyshev FIR Bandpass Filter

To provide some perspective on the results, let's compare the window method to the*optimal Chebyshev FIR filter*(§4.10) for the same length and design specifications above. The following Matlab code illustrates two different bandpass filter designs. The first (different transition bands) illustrates a problem we'll look at. The second (equal transition bands, commented out), avoids the problem.

M = 101; normF = [0 0.3 0.4 0.6 0.8 1.0]; % transition bands different %normF = [0 0.3 0.4 0.6 0.7 1.0]; % transition bands the same amp = [0 0 1 1 0 0]; % desired amplitude in each band [b2,err2] = firpm(M-1,normF,amp); % optimal filter of length MFigure 4.7 shows the frequency response of the Chebyshev FIR filter designed by

`firpm`, to be compared with the window-method FIR filter in Fig.4.6. Note that the upper transition band ``blows up''. This is a well known failure mode in FIR filter design using the Remez exchange algorithm [176,224]. It can be eliminated by narrowing the transition band, as shown in Fig.4.8. There is no error penalty in the transition region, so it is necessary that each one be ``sufficiently narrow'' to avoid this phenomenon. Remember the rule of thumb that the narrowest transition-band possible for a length FIR filter is on the order of , because that's the width of the main-lobe of a length rectangular window (measured between zero-crossings) (§3.1.2). Therefore, this value is quite exact for the transition-widths of FIR bandpass filters designed by the window method using the rectangular window (when the main-lobe fits entirely within the adjacent pass-band and stop-band). For a Hamming window, the window-method transition width would instead be . Thus, we might expect an optimal Chebyshev design to provide transition widths in the vicinity of , but probably not too close to or below In the example above, where the sampling rate was kHz, and the filter length was , we expect to be able to achieve transition bands circa Hz, but not so low as Hz. As we found above, Hz was under-constrained, while Hz was ok, being near the ``Hamming transition width.''

**Next Section:**

Primer on Hilbert Transform Theory

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Matlab Support for the Window Method