# 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,

^{3.1}except 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

**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- Fig.1.3 listed
`simplp`for filtering one block of data, and - Fig.1.4 listed a main program for testing
`simplp`.

`filter`

^{3.3}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*.

`filter`function performs the following iteration over the elements of

`x`to implement any causal, finite-order, linear, time-invariant digital filter:

^{3.4}

where , , , is the length of

`B`, is the length of

`A`, and 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

`filter`function implements

% 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 |

`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.

% 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*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.

% 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 |

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 |

`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 phasewithout 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 filterproducing 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.

`y`to form the ``cropped'' signal

`yss`(`` 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 ,

^{3.6}and is listed in Fig.2.6.

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 |

*amplitude response*(filter gain versus frequency), denotes the

*phase response*(filter phase-shift versus frequency), is frequency in Hz, and 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 . 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. 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):

**The test sinusoids are**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.*sampled*instead of being continuous in time:**The test sinusoid samples are**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.*rounded*to a*finite precision:***Our test sinusoids are**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 successive samples (any*finite duration*, while the ideal sinusoid is infinitely long:*nonrecursive*LTI digital filter), the start-up transient is 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.**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 to , and the number of samples in each test sinusoid`tmax`from to . 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).

## 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 lines = ampin * cos(2*pi*f(k)*t + phasein); % real sinusoidwith the line

s = ampin * e .^ (j*2*pi*f(k)*t + phasein); % complexAnother 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

*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 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 ; 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`= , and the number of samples in each test sinusoid was

`tmax`. 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.

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 evaluated on the unit circle in the complex 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 frequency response of the simplest lowpass filter can be computed using a single line of matlab code:

H = fft([1,1],N);When is a power of 2, the radix-2 FFT algorithm can be used for high-speed execution.

^{3.7}When , 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 . 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 (any causal, finite-order, linear, and time-invariant digital filter, as explicated later in this book).

% 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 |

## Elementary Matlab Problems

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

**Next Section:**

Analysis of a Digital Comb Filter

**Previous Section:**

The Simplest Lowpass Filter