Scribd
Upload
9 views6 pages

DSP Lab4

This document outlines Lab Number 4 for the EC312 Digital Signal Processing course, focusing on sampling audio signals. It includes code for generating and sampling continuous signals at various frequencies, as well as recording audio and analyzing its frequency spectrum. The lab emphasizes the importance of sampling rates in relation to the Nyquist theorem and demonstrates the effects of under-sampling and over-sampling.

Uploaded by

shaheerkz12
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as DOCX, PDF, TXT or read online on Scribd
9 views6 pages

DSP Lab4

This document outlines Lab Number 4 for the EC312 Digital Signal Processing course, focusing on sampling audio signals. It includes code for generating and sampling continuous signals at various frequencies, as well as recording audio and analyzing its frequency spectrum. The lab emphasizes the importance of sampling rates in relation to the Nyquist theorem and demonstrates the effects of under-sampling and over-sampling.

Uploaded by

shaheerkz12
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as DOCX, PDF, TXT or read online on Scribd
You are on page 1/ 6

DEPARTMENT OF COMPUTER &

SOFTWARE ENGINEERING
COLLEGE OF E&ME, NUST, RAWALPINDI

EC312 Digital Signal Processing


Lab Number: 4

SUBMITTED TO:
LE Sundas

SUBMITTED BY:
Shaheer Mukhtiar
Reg # 432017
DE- 44 Dept CE

Submission Date: 4 March 2025


LAB # 04: Sampling Audio Signals

Code:

f = 10;
Fs1 = 2*f;
Fs2 = 1.5*f;
Fs3 = 3*f;
t = 0:0.001:1;

% Continuous signal
Y = cos(2*pi*f*t);

% Sampled signals
t1 = 0:1/Fs1:1;
Y1 = cos(2*pi*f*t1);

t2 = 0:1/Fs2:1;
Y2 = cos(2*pi*f*t2);

t3 = 0:1/Fs3:1;
Y3 = cos(2*pi*f*t3);

% Plot
figure;
subplot(4,1,1);

stem(t1, Y1);
title('Sampled at Fs = 2Fc = Hz');
xlabel('Time (s)');
ylabel('Amplitude');

subplot(4,1,2);
stem(t2, Y2);
title('Sampled at Fs < 2Fc = Hz');
xlabel('Time (s)');
ylabel('Amplitude');

subplot(4,1,3);
stem(t3, Y3);
title('Fs > 2Fc = Hz');
xlabel('Time (s)');
ylabel('Amplitude');

Output:

Part b

Code:

f2 = 50;
f3 = 200;

Fs1 = 2 * f3;
Fs2 = f3 - 50;
Fs3 = 2.5 * f3;

t = 0:0.001:0.1;

% Continuous signal
Y = sin(2 * pi * f2 * t) + cos(2 * pi * f3 * t);

% Sampled signals
t1 = 0:1/Fs1:0.1;
Y1 = sin(2 * pi * f2 * t1) + cos(2 * pi * f3 * t1);

t2 = 0:1/Fs2:0.1;
Y2 = sin(2 * pi * f2 * t2) + cos(2 * pi * f3 * t2);
t3 = 0:1/Fs3:0.1;
Y3 = sin(2 * pi * f2 * t3) + cos(2 * pi * f3 * t3);

% Plot
figure;

subplot(4,1,2);
stem(t1, Y1);
title('Sampled at Fs = 2Fc = Hz');
xlabel('Time (s)');
ylabel('Amplitude');

subplot(4,1,3);
stem(t2, Y2);
title('Sampled at Fs < 2Fc = Hz');
xlabel('Time (s)');
ylabel('Amplitude');

subplot(4,1,4);
stem(t3, Y3);
title('Sampled at Fs > 2Fc = Hz');
xlabel('Time (s)');
ylabel('Amplitude');

Output:
Task 2:

Code:

Fs = 44100;
duration = 5;

recording = audiorecorder(Fs, 16, 1);


disp("Recording...");
recordblocking(recording, duration);
disp("Recording stopped.");

y = getaudiodata(recording);
L = length(y);

Y = fft(y);
P2 = abs(Y/L);
P1 = P2(1:L/2+1);
f = Fs*(0:(L/2))/L;

% Find dominant frequency


[max_val, idx] = max(P1);
peak_freq = f(idx);

% Plot Frequency Spectrum


subplot(4,1,1)
plot(f, P1);
title('Frequency Spectrum of Recorded Audio');
xlabel('Frequency (Hz)');
ylabel('Magnitude');
grid on;

fprintf('Dominant Frequency: %.2f Hz\n', peak_freq);

Fs_Nyquist = 2 * peak_freq;
Fs_Under = peak_freq - 50;
Fs_Over = 2.5 * peak_freq;

[p1, q1] = rat(Fs_Nyquist / Fs);


[p2, q2] = rat(Fs_Under / Fs);
[p3, q3] = rat(Fs_Over / Fs);

y_Nyquist = resample(y, p1, q1);


y_Under = resample(y, p2, q2);
y_Over = resample(y, p3, q3);

t_Nyquist = (0:length(y_Nyquist)-1) / (Fs * p1 / q1);


t_Under = (0:length(y_Under)-1) / (Fs * p2 / q2);
t_Over = (0:length(y_Over)-1) / (Fs * p3 / q3);
subplot(4,1,2);
stem(t_Nyquist, y_Nyquist, '.');
title('Sampling at Fs = 2Fc (Nyquist Rate)');
xlabel('Time (s)');
ylabel('Amplitude');

subplot(4,1,3);
stem(t_Under, y_Under, '.');
title('Sampling at Fs < 2Fc (Aliasing Effect)');
xlabel('Time (s)');
ylabel('Amplitude');

subplot(4,1,4);
stem(t_Over, y_Over, '.');
title('Sampling at Fs > 2Fc (Over-sampling)');
xlabel('Time (s)');
ylabel('Amplitude');

Output:

You might also like