Description
In this lab, you will work on simple Matlab exercises designed to familiarize you with common commands used in digital signal processing. You will
learn to generate sinusoidal signals and design FIR filters using the firpm
routine.
You will learn to load speech signals, listen to them, and do some
simple speech analysis. You will also learn to load images, view them, and do
some simple manipulations.
Most of the Matlab programming will be provided to you, and you will re-use common commands with possibly different
parameters.
The first thing you need to do is to note the last three non-zero digits of
your UIC ID #, say i, j, k. You will be required to use the number NID =
ijk = 100 ∗ i + 10 ∗ j + k in many lab exercises.
1 Discrete-time signals and systems: DTFT display and FIR filter
design
1.1 Generation of sum of two sinusoidal sequences
Define frequencies: ω1 = 0.08 ∗ π + 0.30 ∗ NID ∗ π/1000 and ω2 = π −
ω1. Generate the sequence x[n] = cos(ω1n) + 0.5 cos(ω2n) using program
Plab1 1 SinusoidalSignals.m
% Plab1_1.m Generation of sum of two sinusoidal sequences
%
% Plab1_1.m Generation of sum of two sinusoidal sequences
% Plotting the DFT and DTFT
clf;
NID = input(’Type in N_ID = ’);
w1 = 0.15*pi + 0.2*NID*pi/1000;
w2 = pi – w1;
disp(’Frequency w1 = ’), disp(w1);
disp(’Frequency w2 = ’), disp(w2);
Nsamples = 101;
n = 1:Nsamples;
1
x = 2*cos(w1*n) + cos(w2*n);
Xfft = fft(x);
Xmax1 =max(abs(Xfft));
w = (n-1)*2*pi/Nsamples;
[Xdtft, wgrid] = freqz(x,1,Nsamples);
Xmax2 =max(abs(Xdtft));
stem(n-1, x(n));
xlabel(’Sample number n’);ylabel(’Signal value x’);
axis([0 Nsamples -2 2]);
grid;
title(’Sum of sinusoids’);
disp(’PRESS RETURN for magnitude of DFT and DTFT of x’);
pause
Xdirect = zeros(1,Nsamples);%direct computation of DTFT at Nsamples pointsfor k=1:Nsamples
tem1 = exp(1i*pi*(n-1)*(k-1)/Nsamples);
Xdirect(k) = sum(x.*tem1);
end
w = (n-1)*pi/Nsamples;
plot(w,abs(Xdirect));
xlabel(’frequency \omega’);ylabel(’DTFT magnitude’);
title(’Directly computed DTFT versus frequency \omega’);
pause
subplot(3,1,1)
stem(n-1, abs(Xfft));%Plot the magnitude of DFTof x
xlabel(’frequency index k’);ylabel(’DFT magnitude’);
axis([0 Nsamples-1 0 1.2*Xmax1]);
grid;
title(’DFT versus index k’);
subplot(3,1,2)
stem(w, abs(Xfft));%Plot the magnitude of DFTof x
xlabel(’frequency’);ylabel(’DFT magnitude’);
title(’DFT versus discretized frequency \omega’);
axis([0 2*pi*(Nsamples-1)/Nsamples 0 1.2*Xmax1]);
grid;
2
subplot(3,1,3)
plot(wgrid, abs(Xdtft));%Plot the magnitude of DTFTof x
xlabel(’frequency \omega’);ylabel(’DTFT magnitude’);
title(’DTFT versus frequency \omega’);
axis([0 2*pi*(Nsamples-1)/Nsamples 0 1.2*Xmax1]);
grid;
Q1.1: What are the values of ω1 and ω2 for the sinusoids you generated?
Q1.2: Compare the last three plots you obtained and describe their relation.
1.2 Design a filter using firpm
In Matlab, type “help firpm” to understand the input format for the command to design a FIR filter using the Parks-McClellan program. Run Plab1 2 FIRFilterDe% Plab1_2.m Linear-Phase FIR filter design
%
% Plab1_2_ECE417.m Linear-Phase FIR filter design
%
clf;
NID = input(’Type in N_ID = ’);
fp=0.31+0.14*NID/1000;
fs=1-fp;
Nord=10;% choose even order
h = firpm(Nord, [0 fp fs 1], [1 1 0 0], [1 1]);
[H, w] = freqz(h,1,100);
Hmax=max(abs(H));
plot(w, abs(H));%Plot the magnitude response of filter
xlabel(’frequency’);ylabel(’DFT magnitude’);
title(’Magnitude of H versus frequency’);
axis([0 pi 0 1.2*Hmax]);
grid;
disp(’PRESS RETURN for pole-zero plot of H’);
pause
3
roots(h)
zplane(h);
Q1.3: Display the stem plot of h, with a proper shift, such that the phase
is zero in the passband. Also plot the frequency response using the command
freqz.
Q1.4: Show the plot of the poles and zeros of H(z) using the zplane command.
Q1.5: Use the firpm command to design a bandpass filter of minimum even
order such that it meets the requirements:
first stopband 0 ≤ ω ≤ 0.25π with peak magnitude deviation ≤ 0.025
passband 0.35π ≤ ω ≤ 0.65π with peak magnitude deviation ≤ 0.1
second stopband 0.75π ≤ ω ≤ π with peak magnitude deviation ≤ 0.05
Plot the frequency response using the command freqz. What is the minumum
filter order that you needed?
2 Speech signal Display and Analysis
2.1 Speeech Signal display and analysis
Display the speech signal ece417 audio.wav stored as a wav-file. Identify (as
closely as you can) the start-point and end-point indexes of the word-string
“E C E Four Seventeen”. Run Plab1 3 Speech Display Analysis.m and listen
to the sounds as noted below.
% Plab1_3_ECE417.m Updated 2020-09-06
% Display and examine a speech signal
[y,Fs] = audioread(’ece417_audio.wav’);
fprintf(’Sampling frequency = %.1f Hz\nSampling Period = %.2f microsec\n’clf
soundsc(y,Fs)
plot(y);
title(’Speech signal’);
xlabel(’Time sample index n’)
ylabel(’Amplitude y[n]’)
pause
4
plot(y(12501:19000)); %Plot of the second E sound in ECE
title(’Sound “E”’);
xlabel(’Time sample index n’)
ylabel(’Amplitude y[n]’)
soundsc(y(12501:19000),Fs); % “E”
pause
plot(y(15210:15620)); %Plot of short segment the second E sound in ECE
title(’A few pitch periods of Sound “E”’);
xlabel(’Time sample index n’)
ylabel(’Amplitude y[n]’)
pause
h=firpm(20,[0 0.01 0.15 1.0],[0 0 1 1],[1 1]); %highpass filter IR to remoyf = conv(y,h); %highpass filter output y with DC content removed
soundsc(10*y(5601:7600),Fs); % “s”
plot(yf(5601:7600))
title(’Speech segment with sound “s” with DC content removed’);
xlabel(’Time sample index n’)
ylabel(’Amplitude y[n]’)
pause
subplot(2,1,1)
y1 = y(12501:19000); %voiced segment of y
[Y1,W]=freqz(y1); % Y1 is DTFT of y1
plot(W,abs(Y1));
xlabel(’Frequency \omega’)
ylabel(’Magnitude of DTFT’)
title(’Voiced sound frequency content’);
subplot(2,1,2)
y2 = yf(5601:7600); %unvoiced segment of y with DC removed
[Y2,W]=freqz(y2); % Y2 is DTFT of y2
plot(W,abs(Y2));
xlabel(’Frequency \omega’)
5
ylabel(’Magnitude of DTFT’)
title(’Unvoiced sound frequency content’);
Q1.6 What is the sampling rate of the speech signal?
Q1.7 Compare the frequency content of the voiced and unvoiced parts of
the utterance “s” and “E” described above.
Q1.8 Determine the pitch of the utterance “E” from the plot of a short
segment given in the Matlab code.
2.2 Speech signal denoising
In this part you will perform speech processing to remove noise from a speech
signal ′noisy speech.wav′
. The noisy speech is created by adding noise to the
clean signal ′
ece417 audio.wav′ used in part 2.1. The noise in the noisy speech
is primarily added in the frequency region [0.25π π]. In this exercise you will
remove the noise from (or denoise) the noisy speech using an FIR filter.
Design a suitable FIR filter of order 30 using firpm to denoise the speech.
Choose appropriate care-bands and desired filter magnitude.
Write Matlab code to read the noisy speech file, and process it with the
denoising filter you designed. Show the code in your report.
Q1.9 Listen and compare the denoised speech to the noisy speech and
report the results. Also listen and compare denoised speech to the clean
original speech ′
ece417 audio.wav′
that you used in section 2.1 earlier. Report
your observations.
