Matlab Analysis of the Simplest Lowpass Filter

The example filter implementation listed in Fig.1.3 was written in the C programming language so that all computational details would be fully specified. However, C is a relatively low-level language for signal-processing software. Higher level languages such as matlab make it possible to write powerful programs much faster and more reliably. Even in embedded applications, for which assembly language is typically required, it is usually best to develop and debug the system in matlab beforehand.

The Matlab (R) product by The Mathworks, Inc., is far and away the richest implementation of the matlab language. However, it is very expensive for non-students, so you may at some point want to consider the free, open-source alternative called Octave. All examples in this chapter will work in either Matlab or Octave,3.1except that some plot-related commands may need to be modified. The term matlab (not capitalized) will refer henceforth to either Matlab or Octave, or any other compatible implementation of the matlab language.3.2

This chapter provides four matlab programming examples to complement the mathematical analysis of §1.3:

2.1:
Filter implementation
2.2:
Simulated sine-wave analysis
2.3:
Simulated complex sine-wave analysis
2.4:
Practical frequency-response analysis
In all four examples, the simplest lowpass filter, $ y(n) = x(n) + x(n - 1)$, is used as the specific filter for implementation or analysis, and the results obtained by simulations are compared to those obtained from theory in §1.3.

Note: The reader is expected to know (at least some) matlab before proceeding. See, for example, the Matlab Getting Started documentation, or just forge ahead and use the examples below to start learning matlab. (It is very readable, as computer languages go.) To skip over the matlab examples for now, proceed to Chapter 3.

Matlab Filter Implementation

In this section, we will implement (in matlab) the simplest lowpass filter

$\displaystyle y(n) = x(n) + x(n-1),\;n=1,2,\ldots,N
$

(from Eq.$ \,$(1.1)). For the simplest lowpass filter, we had two program listings:
  • Fig.1.3 listed simplp for filtering one block of data, and
  • Fig.1.4 listed a main program for testing simplp.
In matlab, there is a built-in function called filter3.3 which will implement simplp as a special case. The syntax is
        y = filter (B, A, x)
where
x is the input signal (a vector of any length),
y is the output signal (returned equal in length to x),
A is a vector of filter feedback coefficients, and
B is a vector of filter feedforward coefficients.
The filter function performs the following iteration over the elements of x to implement any causal, finite-order, linear, time-invariant digital filter:3.4
$\displaystyle \texttt{y}(n)$ $\displaystyle =$ $\displaystyle \sum_{k=0}^{M_n} \texttt{B}(k+1) \texttt{x}(n-k)$  
  $\displaystyle -$ $\displaystyle \sum_{k=1}^{N_n} \texttt{A}(k+1) \texttt{y}(n-k), \quad n=1,2,\ldots,N_x
\protect$ (3.1)

where $ N_x \isdef \texttt{length(x)}$, $ N_n\isdef \min\{N,n-1\}$, $ M_n\isdef \min\{M,n-1\}$, $ M+1$ is the length of B, $ N+1$ is the length of A, and $ \texttt{A}(1)$ is assumed to be 1. (Otherwise, B and A are divided through by A(1). Note that A(1) is not used in Eq.$ \,$(2.1).) The relatively awkward indexing in Eq.$ \,$(2.1) is due to the fact that, in matlab, all array indices start at 1, not 0 as in most C programs.

Note that Eq.$ \,$(2.1) could be written directly in matlab using two for loops (as shown in Fig.3.2). However, this would execute much slower because the matlab language is interpreted, while built-in functions such as filter are pre-compiled C modules. As a general rule, matlab programs should avoid iterating over individual samples whenever possible. Instead, whole signal vectors should be processed using expressions involving vectors and matrices. In other words, algorithms should be ``vectorized'' as much as possible. Accordingly, to get the most out of matlab, it is necessary to know some linear algebra [58].

The simplest lowpass filter of Eq.$ \,$(1.1) is nonrecursive (no feedback), so the feedback coefficient vector A is set to 1.3.5 Recursive filters will be introduced later in §5.1. The minus sign in Eq.$ \,$(2.1) will make sense after we study filter transfer functions in Chapter 6.

The feedforward coefficients needed for the simplest lowpass filter are

$\displaystyle \texttt{B}= [ 1 , 1 ].
$

With these settings, the filter function implements

\begin{eqnarray*}
\texttt{y}(1) &=& \texttt{B}(1) \cdot \texttt{x}(1)\\
\texttt...
...texttt{x}(n-1), \qquad n=2,3,\ldots,\texttt{length}(\texttt{x}).
\end{eqnarray*}

Figure 2.1: Main matlab program for implementing the simplest low-pass filter $ y(n) = x(n) + x(n - 1)$.

 
% simplpm1.m - matlab main program implementing
%              the simplest lowpass filter:
%
%                    y(n) = x(n)+x(n-1)}

N=10;       % length of test input signal
x = 1:N;    % test input signal (integer ramp)
B = [1,1];  % transfer function numerator
A = 1;      % transfer function denominator

y = filter(B,A,x);

for i=1:N
  disp(sprintf('x(%d)=%f\ty(%d)=%f',i,x(i),i,y(i)));
end

% Output:
%   octave:1> simplpm1
%   x(1)=1.000000       y(1)=1.000000
%   x(2)=2.000000       y(2)=3.000000
%   x(3)=3.000000       y(3)=5.000000
%   x(4)=4.000000       y(4)=7.000000
%   x(5)=5.000000       y(5)=9.000000
%   x(6)=6.000000       y(6)=11.000000
%   x(7)=7.000000       y(7)=13.000000
%   x(8)=8.000000       y(8)=15.000000
%   x(9)=9.000000       y(9)=17.000000
%   x(10)=10.000000     y(10)=19.000000

A main test program analogous to Fig.1.4 is shown in Fig.2.1. Note that the input signal is processed in one big block, rather than being broken up into two blocks as in Fig.1.4. If we want to process a large sound file block by block, we need some way to initialize the state of the filter for each block using the final state of the filter from the preceding block. The filter function accommodates this usage with an additional optional input and output argument:

        [y, Sf] = filter (B, A, x, Si)
Si denotes the filter initial state, and Sf denotes its final state. A main program illustrating block-oriented processing is given in Fig.2.2.

Figure: Block-oriented version of the matlab program in Fig.2.1.

 
% simplpm2.m - block-oriented version of simplpm1.m

N=10;       % length of test input signal
NB=N/2;     % block length
x = 1:N;    % test input signal
B = [1,1];  % feedforward coefficients
A = 1;      % feedback coefficients (no-feedback case)

[y1, Sf] = filter(B,A,x(1:NB));      % process block 1
      y2 = filter(B,A,x(NB+1:N),Sf); % process block 2

for i=1:NB   % print input and output for block 1
  disp(sprintf('x(%d)=%f\ty(%d)=%f',i,x(i),i,y1(i)));
end

for i=NB+1:N % print input and output for block 2
  disp(sprintf('x(%d)=%f\ty(%d)=%f',i,x(i),i,y2(i-NB)));
end


Simulated Sine-Wave Analysis in Matlab

In this section, we will find the frequency response of the simplest lowpass filter

$\displaystyle y(n) = x(n) + x(n-1),\quad n=1,2,\ldots,N.
$

using simulated sine-wave analysis carried out by a matlab program. This numerical approach complements the analytical approach followed in §1.3. Figure 2.3 gives a listing of the main script which invokes the sine-wave analysis function swanal listed in Fig.2.4. The plotting/printing utilities swanalmainplot and swanalplot are listed in Appendix J starting at §J.13.

Figure 2.3: Main matlab program for computing the frequency response of the simplest low-pass filter $ y(n) = x(n) + x(n - 1)$ by means of simulated sine-wave analysis.

 
% swanalmain.m - matlab program for simulated sine-wave
%             analysis on the simplest lowpass filter:
%
%                 y(n) = x(n)+x(n-1)}

B = [1,1]; % filter feedforward coefficients
A = 1;     % filter feedback coefficients (none)

N=10;           % number of sinusoidal test frequencies
fs = 1;         % sampling rate in Hz (arbitrary)

fmax = fs/2;    % highest frequency to look at
df = fmax/(N-1);% spacing between frequencies
f = 0:df:fmax;  % sampled frequency axis
dt = 1/fs;      % sampling interval in seconds
tmax = 10;      % number of seconds to run each sine test
t = 0:dt:tmax;  % sampled time axis
ampin = 1;      % test input signal amplitude
phasein = 0;    % test input signal phase

[gains,phases] = swanal(t,f/fs,B,A); % sine-wave analysis

swanalmainplot;    % final plots and comparison to theory

Figure 2.4: Matlab function for performing simulated sine-wave analysis.

 
function [gains,phases] = swanal(t,f,B,A)
% SWANAL - Perform sine-wave analysis on filter B(z)/A(z)

ampin = 1;      % input signal amplitude
phasein = 0;    % input signal phase

N = length(f);       % number of test frequencies
gains = zeros(1,N);  % pre-allocate amp-response array
phases = zeros(1,N); % pre-allocate phase-response array
if length(A)==1
  ntransient=length(B)-1; % no. samples to steady state
else
  error('Need to set transient response duration here');
end

for k=1:length(f)    % loop over analysis frequencies
  s = ampin*cos(2*pi*f(k)*t+phasein); % test sinusoid
  y = filter(B,A,s); % run it through the filter
  yss = y(ntransient+1:length(y)); % chop off transient
  % measure output amplitude as max (SHOULD INTERPOLATE):
  [ampout,peakloc] = max(abs(yss)); % ampl. peak & index
  gains(k) = ampout/ampin;  % amplitude response
  if ampout < eps  % eps returns "machine epsilon"
    phaseout=0;    % phase is arbitrary at zero gain
  else
    sphase = 2*pi*f(k)*(peakloc+ntransient-1);
    % compute phase by inverting sinusoid (BAD METHOD):
    phaseout = acos(yss(peakloc)/ampout) - sphase;
    phaseout = mod2pi(phaseout); % reduce to [-pi,pi)
  end
  phases(k) = phaseout-phasein;
  swanalplot; % signal plotting script
end

In the swanal function (Fig.2.4), test sinusoids are generated by the line

        s = ampin * cos(2*pi*f(k)*t + phasein);
where amplitude, frequency (Hz), and phase (radians) of the sinusoid are given be ampin, f(k), and phasein, respectively. As discussed in §1.3, assuming linearity and time-invariance (LTI) allows us to set
        ampin = 1;      % input signal amplitude
        phasein = 0;    % input signal phase
without loss of generality. (Note that we must also assume the filter is LTI for sine-wave analysis to be a general method for characterizing the filter's response.) The test sinusoid is passed through the digital filter by the line
        y = filter(B,A,s); % run s through the filter
producing the output signal in vector y. For this example (the simplest lowpass filter), the filter coefficients are simply
        B = [1,1]; % filter feedforward coefficients
        A = 1;     % filter feedback coefficients (none)
The coefficient A(1) is technically a coefficient on the output signal itself, and it should always be normalized to 1. (B and A can be multiplied by the same nonzero constant to carry out this normalization when necessary.)

Figure 2.5 shows one of the intermediate plots produced by the sine-wave analysis routine in Fig.2.4. This figure corresponds to Fig.1.6 in §1.3 (see page [*]). In Fig.2.5a, we see samples of the input test sinusoid overlaid with the continuous sinusoid represented by those samples. Figure 2.5b shows only the samples of the filter output signal: While we know the output signal becomes a sampled sinusoid after the one-sample transient response, we do not know its amplitude or phase until we measure it; the underlying continuous signal represented by the samples is therefore not plotted. (If we really wanted to see this, we could use software for bandlimited interpolation [91], such as Matlab's interp function.) A plot such as Fig.2.5 is produced for each test frequency, and the relative amplitude and phase are measured between the input and output to form one sample of the measured frequency response, as discussed in §1.3.

Figure 2.5: Input and output signals for the sixth test frequency $ f(k)=f_s/4$ in the simulated sine-wave analysis of the simplest lowpass filter.
\includegraphics[width=\twidth ]{eps/swanalsix}

Next, the one-sample start-up transient is removed from the filter output signal y to form the ``cropped'' signal yss (``$ y$ steady state''). The final task is to measure the amplitude and phase of the yss. Output amplitude estimation is done in swanal by the line

        [ampout,peakloc] = max(abs(yss));
Note that the peak amplitude found in this way is approximate, since the true peak of the output sinusoid generally occurs between samples. We will find the output amplitude much more accurately in the next two sections. We store the index of the amplitude peak in peakloc so it can be used to estimate phase in the next step. Given the output amplitude ampout, the amplitude response of the filter at frequency f(k) is given by
        gains(k) = ampout/ampin;
The last step of swanal in Fig.2.4 is to estimate the phase of the cropped filter output signal yss. Since we will have better ways to accomplish this later, we use a simplistic method here based on inverting the sinusoid analytically:
        phaseout = acos(yss(peakloc)/ampout) ...
                     - 2*pi*f(k)*(peakloc+ntransient-1);
        phaseout = mod2pi(phaseout); % reduce to [-pi,pi)
Again, this is only an approximation since peakloc is only accurate to the nearest sample. The mod2pi utility reduces its scalar argument to the range $ [-\pi ,\pi )$,3.6 and is listed in Fig.2.6.
Figure 2.6: Utility (matlab) for reducing a radian phase angle to $ [-\pi ,\pi )$.

 
function [y] = mod2pi(x)
% MOD2PI - Reduce x to the range [-pi,pi)
  y=x;
  twopi = 2*pi;
  while y >= pi, y = y - twopi; end
  while y < -pi, y = y + twopi; end

In summary, the sine-wave analysis measures experimentally the gain and phase-shift of the digital filter at selected frequencies, thus measuring the frequency response of the filter at those frequencies. It is interesting to compare these experimental results with the closed-form expressions for the frequency response derived in §1.3.2. From Equations (1.6-1.7) we have

\begin{eqnarray*}
G(f) &=& 2\cos(\pi f /f_s) \\ % \qquad \hbox{(Amplitude Response)}\\
\Theta(f) &=& - \pi f /f_s% \qquad \hbox{(Phase Response)}
\end{eqnarray*}

where $ G(f)$ denotes the amplitude response (filter gain versus frequency), $ \Theta(f)$ denotes the phase response (filter phase-shift versus frequency), $ f$ is frequency in Hz, and $ f_s=1/T$ denotes the sampling rate. Both the amplitude response and phase response are real-valued functions of (real) frequency, while the frequency response is the complex function of frequency given by $ H(f) = G(f)e^{j\Theta(f)}$.

Figure 2.7 shows overlays of the measured and theoretical results. While there is good general agreement, there are noticeable errors in the measured amplitude and phase-response curves. Also, the phase-response error tends to be large when there is an amplitude response error, since the phase calculation used depends on knowledge of the amplitude response.

Figure 2.7: Overlay of measured and theoretical frequency response when $ f_{\hbox {max}}=f_s/2$.
\includegraphics[width=\twidth ]{eps/swanalmain}

It is important to understand the source(s) of deviation between the measured and theoretical values. Our simulated sine-wave analysis deviates from an ideal sine-wave analysis in the following ways (listed in ascending order of importance):

  1. The test sinusoids are sampled instead of being continuous in time: It turns out this is a problem only for frequencies at half the sampling rate and above. Below half the sampling rate, sampled sinusoids contain exactly the same information as continuous sinusoids, and there is no penalty whatsoever associated with discrete-time sampling itself.

  2. The test sinusoid samples are rounded to a finite precision: Digitally sampled sinusoids do suffer from a certain amount of round-off error, but Matlab and Octave use double-precision floating-point numbers by default (64 bits). As a result, our samples are far more precise than could be measured acoustically in the physical world. This is not a visible source of error in Fig.2.7.

  3. Our test sinusoids are finite duration, while the ideal sinusoid is infinitely long: This can be a real practical limitation. However, we worked around it completely by removing the start-up transient. For the simplest lowpass filter, the start-up transient is only one sample long. More generally, for digital filters expressible as a weighted sum of $ M$ successive samples (any nonrecursive LTI digital filter), the start-up transient is $ M-1$ samples long. When we consider recursive digital filters, which employ output feedback, we will no longer be able to remove the start-up transient exactly, because it generally decays exponentially instead of being finite in length. However, even in that case, as long as the recursive filter is stable, we can define the start-up transient as some number of time-constants of exponential decay, thereby making the approximation error as small as we wish, such as less than the round-off error.

  4. We measured the output amplitude and phase at a signal peak measured only to the nearest sample: This is the major source of error in our simulation. The program of Fig.2.3 measures the filter output amplitude very crudely as the maximum magnitude. In general, even for this simple filter, the maximum output signal amplitude occurs between samples. To measure this, we would need to use what is called bandlimited interpolation [91]. It is possible and practical to make the error arbitrarily small by increasing the sampling rate by some factor and finishing with quadratic interpolation of the three samples about the peak magnitude. Similar remarks apply to the measured output phase.

    The need for interpolation is lessened greatly if the sampling rate is chosen to be unrelated to the test frequencies (ideally so that the number of samples in each sinusoidal period is an irrational number). Figure 2.8 shows the measured and theoretical results obtained by changing the highest test frequency fmax from $ f_s/2$ to $ f_s/2.34567$, and the number of samples in each test sinusoid tmax from $ 10$ to $ 100$. For these parameters, at least one sample falls very close to a true peak of the output sinusoid at each test frequency.

    It should also be pointed out that one never estimates signal phase in practice by inverting the closed-form functional form assumed for the signal. Instead, we should estimate the delay of each output sinusoid relative to the corresponding input sinusoid. This leads to the general topic of time delay estimation [12]. Under the assumption that the round-off error can be modeled as ``white noise'' (typically this is an accurate assumption), the optimal time-delay estimator is obtained by finding the (interpolated) peak of the cross-correlation between the input and output signals. For further details on cross-correlation, a topic in statistical signal processing, see, e.g., [77,87].

    Using the theory presented in later chapters, we will be able to compute very precisely the frequency response of any LTI digital filter without having to resort to bandlimited interpolation (for measuring amplitude response) or time-delay estimation (for measuring phase).

Figure 2.8: Overlay of measured and theoretical frequency response when $ f_{\hbox {max}}=f_s/2.34567$.
\includegraphics[width=\twidth ]{eps/swanalrmain}


Complex Sine-Wave Analysis

To illustrate the use of complex numbers in matlab, we repeat the previous sine-wave analysis of the simplest lowpass filter using complex sinusoids instead of real sinusoids.

Only the sine-wave analysis function needs to be rewritten, and it appears in Fig.2.10. The initial change is to replace the line

  s = ampin * cos(2*pi*f(k)*t + phasein); % real sinusoid
with the line
  s = ampin * e .^ (j*2*pi*f(k)*t + phasein); % complex
Another change in Fig.2.10 is that the plotsignals option is omitted, since a complex signal plot requires two real plots. This option is straightforward to restore if desired.

In the complex-sinusoid case, we find that measuring the amplitude and phase of the output signal is greatly facilitated. While we could use the previous method on either the real part or imaginary part of the complex output sinusoid, it is much better to measure its instananeous amplitude and instananeous phase by means of the formulas

\begin{eqnarray*}
A &=& \left\vert A e^{j\theta}\right\vert\\
\theta &=& \angle{A e^{j\theta}}.
\end{eqnarray*}

Furthermore, since we should obtain the same answer for each sample, we can average the results to minimize noise due to round-off error:

  ampout = mean(abs(yss)); % avg instantaneous amplitude
  gains(k) = ampout/ampin; % amplitude response sample
  sss = s(ntransient+1:length(y)); % chop input like output
  phases(k) = mean(mod2pi(angle(yss.*conj(sss))));
The expression angle(yss.*conj(sss)) in the last line above produces a vector of estimated filter phases which are the same for each sample (to within accumulated round-off errors), because the term $ e^{j2\pi f(k) nT+\phi}$ in yss is canceled by the conjugate of that term in conj(sss). We must be certain that the filter phase-shift is well within the interval $ [-\pi ,\pi )$; otherwise a call to mod2pi would be necessary for each element of angle(yss.*conj(sss)).

The final measured frequency response is plotted in Fig.2.9. The test conditions were as in Fig.2.7, i.e., the highest test frequency was fmax = $ f_s/2$, and the number of samples in each test sinusoid was tmax $ = 10$. Unlike the real sine-wave analysis results in Fig.2.7, there is no visible error associated with complex sine-wave analysis. Because instantaneous amplitude and phase are available from every sample of a complex sinusoid, there is no need for signal interpolation of any kind. The only source of error is now round-off error, and even that can be ``averaged out'' to any desired degree by enlarging the number of samples in the complex sinusoids used to probe the system.

Figure 2.9: Overlay of measured and theoretical frequency responses of the simplest lowpass filter, obtained using complex sine-wave analysis.
\includegraphics[width=\twidth ]{eps/swanalcmain}

This example illustrates some of the advantages of complex sinusoids over real sinusoids. Note also the ease with which complex vector quantities are manipulated in the matlab language.

Figure 2.10: Matlab function for performing complex sine-wave analysis.

 
function [gains,phases] = swanalc(t,f,B,A)
% SWANALC - Perform COMPLEX sine-wave analysis on the
%           digital filter having transfer function
%           H(z) = B(z)/A(z)

ampin = 1;      % input signal amplitude
phasein = 0;    % input signal phase

N = length(f);       % number of test frequencies
gains = zeros(1,N);  % pre-allocate amp-response array
phases = zeros(1,N); % pre-allocate phase-response array

if length(A)==1, ntransient=length(B)-1, else
  error('Need to set transient response duration here');
end

for k=1:length(f)    % loop over analysis frequencies
  s = ampin*e.^(j*2*pi*f(k)*t+phasein); % test sinusoid
  y = filter(B,A,s); % run it through the filter
  yss = y(ntransient+1:length(y)); % chop off transient
  ampout = mean(abs(yss)); % avg instantaneous amplitude
  gains(k) = ampout/ampin; % amplitude response sample
  sss = s(ntransient+1:length(y)); % align with yss
  phases(k) = mean(mod2pi(angle(yss.*conj(sss))));
end


Practical Frequency-Response Analysis

The preceding examples were constructed to be tutorial on the level of this (introductory) part of this book, specifically to complement the previous chapter with matlab implementations of the concepts discussed. A more typical frequency response analysis, as used in practice, is shown in Fig.2.11.

A comparison of computed and theoretical frequency response curves is shown in Fig.2.12. There is no visible difference, and the only source of error is computational round-off error. Not only is this method as accurate as any other, it is by far the fastest, because it uses the Fast Fourier Transform (FFT).

This FFT method for computing the frequency response is based on the fact that the frequency response equals the filter transfer function $ H(z)=B(z)/A(z)$ evaluated on the unit circle $ z=e^{j\omega
T}$ in the complex $ z$ plane. We will get to these concepts later. For now, just note the ease with which we can compute the frequency response numerically in matlab. In fact, the length $ N$ frequency response of the simplest lowpass filter $ y(n) = x(n) + x(n - 1)$ can be computed using a single line of matlab code:

        H = fft([1,1],N);
When $ N$ is a power of 2, the radix-2 FFT algorithm can be used for high-speed execution.3.7 When $ N>2$, the FFT input is automatically ``zero padded'' in the time domain, resulting in interpolation of the frequency response [84].3.8 In other words, the above line of code is equivalent to
        H = fft([1,1,zeros(1,N-2)]);
when $ N\ge 2$. The code in Fig.2.11 carries out explicit zero-padding for clarity.

In both Matlab and Octave, there is a built-in function freqz which uses this FFT method for calculating the frequency response for almost any digital filter $ B(z)/A(z)$ (any causal, finite-order, linear, and time-invariant digital filter, as explicated later in this book).

Figure: Main program in matlab for finding the frequency response of the simplest low-pass filter $ y(n) = x(n) + x(n - 1)$ using the FFT. See §J.13 for the final plotting utility.

 
% simplpnfa.m - matlab program for frequency analysis
%               of the simplest lowpass filter:
%
%                    y(n) = x(n)+x(n-1)}
%
%               the way people do it in practice.

B = [1,1]; % filter feedforward coefficients
A = 1;     % filter feedback coefficients

N=128;     % FFT size = number of COMPLEX sinusoids
fs = 1;    % sampling rate in Hz (arbitrary)

Bzp = [B, zeros(1,N-length(B))]; % zero-pad for the FFT

H = fft(Bzp);   % length(Bzp) should be a power of 2

if length(A)>1  % we're not using this here,
  Azp = [A,zeros(1,N-length(A))]; % but show it anyway.
  % [Should guard against fft(Azp)==0 for some index]
  H = H ./ fft(A,N); % denominator from feedback coeffs
end

% Discard the frequency-response samples at
% negative frequencies, but keep the samples at
% dc and fs/2:

nnfi = (1:N/2+1);     % nonnegative-frequency indices
Hnnf = H(nnfi);       % lose negative-frequency samples
nnfb = nnfi-1;        % corresponding bin numbers
f = nnfb*fs/N;        % frequency axis in Hz
gains = abs(Hnnf);    % amplitude response
phases = angle(Hnnf); % phase response

plotfile = '../eps/simplpnfa.eps';
swanalmainplot;    % final plots and comparison to theory

Figure 2.12: Overlay of theoretical and numerically determined frequency response using the FFT method.
\includegraphics[width=\twidth ]{eps/simplpnfa}


Elementary Matlab Problems

See http://ccrma.stanford.edu/~jos/filtersp/Elementary_Matlab_Problems.html.


Next Section:
Analysis of a Digital Comb Filter
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The Simplest Lowpass Filter