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FFT Analysis of two signals

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Hello everyone,
I have a beginners question about FFT analysis. I have two signals and I want to see the amplitudespectrum of them. I used the following code which was derived from the program desribed under mathworks.
Fs = 200; % Sampling frequency
T = 1/Fs; % Sample time
L = length (marker(1,2).C7); % Length of signal
t = (0:L-1)*T; % Time vector
NFFT = 2^nextpow2(L); % Next power of 2 from length of y
y= marker(1,2).C7;
Y = fft(y,NFFT)/L;
f = Fs/2*linspace(0,1,NFFT/2+1);
% Plot single-sided amplitude spectrum.
xlabel('Frequency (Hz)')
marker(1,2).C7 is the signal I want to analyse.
The following problem occured: If I run this program on two different signals I get the same amplitudespectrum for both although the signals are different. This cannnot be true as I wanted to compare the amplitudespectrum of the signal before and after it was filtered. Although they were filtered and they looked different when I ploted them the amplitudespectrum plotted with the above code showed no differences at all. Can anybody point out where I made a mistake?
Thank you for your help,
David Young
David Young on 14 Mar 2012
I think you may need to show the complete code that compares the two signals.

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Accepted Answer

Wayne King
Wayne King on 15 Mar 2012
Let's assume that x is one signal and y is the other signal. If you have the Signal Processing Toolbox, you can quickly compare their power spectral densities with the following. fs below is your sampling frequency, so you need to put the correct value in for that.
psdestx = psd(spectrum.periodogram,x,'Fs',fs,'NFFT',length(x));
psdesty = psd(spectrum.periodogram,y,'Fs',fs,'NFFT',length(y));
title('PSD of x'); xlabel('Hz'); ylabel('dB/Hz');
title('PSD of y'); xlabel('Hz'); ylabel('dB/Hz');
  1 Comment
Christoph Bauer
Christoph Bauer on 15 Mar 2012
Thank you, that solved my problem

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More Answers (1)

Christoph Bauer
Christoph Bauer on 15 Mar 2012
I want to compare the same signal before and after filtering.
There is no other code to compare the signals yet. The signal was measured and later filtered in another software, using a butterworth 4th order filter. After that I imported the signal into matlab, first the raw signal and then the filtered signal. Then I ploted both and used the above code to compare the signal before and after filtering.


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