HW11 Aliasing Example: Difference between revisions

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So what happens when you sample as the Nyquist-Shannon thereom suggests, and what happens if you dont? I hate Matlab, but in the self-sacrificing spirit I have towards you, my dear reader, I will use everything in my power including Matlab to clarify things. Hence:
So what happens when you sample as the Nyquist-Shannon thereom suggests, and what happens if you dont? I hate Matlab, but in the self-sacrificing spirit I have towards you, my dear reader, I will use everything in my power including Matlab to clarify things. Hence:


Octave Script:
Here is a sin wave,<math>sin(2pit)</math>
frequency = 1;

sample = 1.4;
Here is that sine wave sampled at points twice it's frequency
T = 1/sample %period;

t = 0:0.01:3.14; %graph accuracy to .01;
Here is that sine wave sampled at the same frequency as the wave
signal = cos(2*pi*t); %original signal;

ts = 0:T:3.14;
Here is the correctly sampled sine wave reconstructed
sampledpoints = cos(2*pi*frequency*ts);

reconstructed = cos(2*pi*(frequency-sample)*t);
Here is the insufficiently sampled sine wave reconstructed
plot(t, signal, 'g', ts, sampledpoints, 'p',t, reconstructed, 'b');

legend('Original Signal', 'Sampled Points', 'Reconstructed Signal');
xlabel('Time');
ylabel('Amplitude');
title('Aliasing Example');
So it has the same shape, but as you can see the frequency is alot lower than the original. Basically what not sampling enough does is that it recognizes multiple frequencies as the same frequency, so you lose crucial details in the reproduction of a sound.
So it has the same shape, but as you can see the frequency is alot lower than the original. Basically what not sampling enough does is that it recognizes multiple frequencies as the same frequency, so you lose crucial details in the reproduction of a sound.

Revision as of 22:57, 29 November 2007

Aliasing - This is when you're sampling a function, but you don't sample often enough to reconstruct the waveform as it was before.

How often is often enough? Well, the Nyquist-Shannon sampling thereom says twice as much as the highest frequency. To be exact, the theorem states: "Exact reconstruction of a continuous-time baseband signal from its samples is possible if the signal is bandlimited and the sampling frequency is greater than twice the signal bandwidth."

So what happens when you sample as the Nyquist-Shannon thereom suggests, and what happens if you dont? I hate Matlab, but in the self-sacrificing spirit I have towards you, my dear reader, I will use everything in my power including Matlab to clarify things. Hence:

Octave Script: frequency = 1; sample = 1.4; T = 1/sample %period; t = 0:0.01:3.14; %graph accuracy to .01; signal = cos(2*pi*t); %original signal; ts = 0:T:3.14; sampledpoints = cos(2*pi*frequency*ts); reconstructed = cos(2*pi*(frequency-sample)*t); plot(t, signal, 'g', ts, sampledpoints, 'p',t, reconstructed, 'b'); legend('Original Signal', 'Sampled Points', 'Reconstructed Signal'); xlabel('Time'); ylabel('Amplitude'); title('Aliasing Example');

So it has the same shape, but as you can see the frequency is alot lower than the original. Basically what not sampling enough does is that it recognizes multiple frequencies as the same frequency, so you lose crucial details in the reproduction of a sound.