###
Filtering and Windowing the Ideal

Hilbert-Transform Impulse Response

Let denote the convolution kernel of the continuous-time Hilbert transform from (4.17) above:

Convolving a real signal with this kernel produces the imaginary part of the corresponding analytic signal. The way the ``window method'' for digital filter design is classically done is to simply

*sample*the ideal impulse response to obtain and then window it to give . However, we know from above (

*e.g.*, §4.5.2) that we need to provide

*transition bands*in order to obtain a reasonable design. A single-sideband filter needs a transition band between dc and , or higher, where denotes the main-lobe width (in rad/sample) of the window we choose, and a second transition band is needed between and .

Note that we cannot allow a time-domain sample at time **0**
in
(4.22) because it would be infinity. Instead, time **0**
should be taken to lie between two samples, thereby introducing a
small non-integer advance or delay. We'll choose a half-sample delay.
As a result, we'll need to delay the real-part filter by half a sample
as well when we make a complete single-sideband filter.

The matlab below illustrates the design of an FIR Hilbert-transform
filter by the window method using a *Kaiser window*. For a more
practical illustration, the sampling-rate assumed is set to
Hz instead of being normalized to 1 as usual. The
Kaiser-window
parameter is set to
, which normally gives
``pretty good'' audio performance (*cf.* Fig.3.28). From
Fig.3.28, we see that we can expect a stop-band attenuation
better than
dB. The choice of
, in setting the
time-bandwidth product of the Kaiser window, determines both the
stop-band rejection and the transition bandwidths required by our FIR
frequency response.

M = 257; % window length = FIR filter length (Window Method) fs = 22050; % sampling rate assumed (Hz) f1 = 530; % lower pass-band limit = transition bandwidth (Hz) beta = 8; % beta for Kaiser window for decent side-lobe rejectionRecall that, for a rectangular window, our minimum transition bandwidth would be Hz, and for a Hamming window, Hz. In this example, using a Kaiser window with ( ), the main-lobe width is on the order of Hz, so we expect transition bandwidths of this width. The choice above should therefore be sufficient, but not ``tight''.

^{5.8}For each doubling of the filter length (or each halving of the sampling rate), we may cut in half.

#### Matlab, Continued

Given the above design parameters, we compute some derived parameters as follows:

fn = fs/2; % Nyquist limit (Hz) f2 = fn - f1; % upper pass-band limit N = 2^(nextpow2(8*M)); % large FFT for interpolated display k1 = round(N*f1/fs); % lower band edge in bins if k1<2, k1=2; end; % cannot have dc or fn response kn = N/2 + 1; % bin index at Nyquist limit (1-based) k2 = kn-k1+1; % high-frequency band edge f1 = k1*fs/N % quantized band-edge frequencies f2 = k2*fs/NSetting the upper transition band the same as the low-frequency band ( ) provides an additional benefit: the symmetry of the desired response about cuts the computational expense of the filter in

*half*, because it forces every other sample in the impulse response to be zero [224, p. 172].

^{5.9}

#### Kaiser Window

With the filter length and Kaiser window as given above, we may compute the Kaiser window itself in matlab via

w = kaiser(M,beta)'; % Kaiser window in "linear phase form"The spectrum of this window (zero-padded by more than a factor of 8) is shown in Fig.4.9 (full magnitude spectrum) and Fig.4.10 (zoom-in on the main lobe).

####
Windowing a Desired Impulse Response Computed by the

Frequency Sampling Method

The next step is to apply our Kaiser window to the ``desired'' impulse
response, where ``desired'' means a time-shifted (by 1/2 sample) and
bandlimited (to introduce transition bands) version of the ``ideal''
impulse response in (4.22). In principle, we are using the
*frequency-sampling method* (§4.4) to prepare a
desired FIR filter of length
as the inverse FFT of a desired
frequency response prepared by direct Fourier intuition. This long
FIR filter is then ``windowed'' down to length
to give us our
final FIR filter designed by the window method.

If the smallest transition bandwidth is
Hz, then the FFT size
should satisfy
. Otherwise, there may be too much time
aliasing in the desired impulse response.^{5.10} The only non-obvious
part in the matlab below is ```.^8`

'' which smooths the taper to
zero and looks better on a log magnitude scale. It would also make
sense to do a linear taper on a dB scale which corresponds to
an exponential taper to zero.

H = [ ([0:k1-2]/(k1-1)).^8,ones(1,k2-k1+1),... ([k1-2:-1:0]/(k1-1)).^8, zeros(1,N/2-1)];Figure 4.11 shows our desired amplitude response so constructed.

Now we inverse-FFT the desired frequency response to obtain the desired impulse response:

h = ifft(H); % desired impulse response hodd = imag(h(1:2:N)); % This should be zero ierr = norm(hodd)/norm(h); % Look at numerical round-off error % Typical value: ierr = 4.1958e-15 % Also look at time aliasing: aerr = norm(h(N/2-N/32:N/2+N/32))/norm(h); % Typical value: 4.8300e-04The real part of the desired impulse response is shown in Fig.4.12, and the imaginary part in Fig.4.13.

Now use the Kaiser window to time-limit the desired impulse response:

% put window in zero-phase form: wzp = [w((M+1)/2:M), zeros(1,N-M), w(1:(M-1)/2)]; hw = wzp .* h; % single-sideband FIR filter, zero-centered Hw = fft(hw); % for results display: plot(db(Hw)); hh = [hw(N-(M-1)/2+1:N),hw(1:(M+1)/2)]; % caual FIR % plot(db(fft([hh,zeros(1,N-M)]))); % freq resp plot

Figure 4.14 and Fig.4.15
show the normalized dB magnitude frequency response of our
final FIR filter consisting of the
nonzero samples of
`hw`.

**Next Section:**

More General FIR Filter Design

**Previous Section:**

Primer on Hilbert Transform Theory