How i do i get the value of a fft bin?

 Hello Community!

First of all, half of this code is taken from this website. Because i needed a part of it since it is for a project. Anyway to my problem: If look at the line, where i put the comment 'L,M,H', where i am defining the frequency range. Afterwards i use the "find", so that the code knows, which value it should take in Low indices, Medium Indices and High Indices and so on... Then values are being averaged and the value from the variable ML is 0.1254. How do i know, which values are being used to get this number. I want to see it in the command window.

 clear all;
 Fs = 1000;            % Sampling frequency
 T = 1/Fs;             % Sampling period
 L = 1000;             % Length of signal
 t = (0:L-1)*T;        % Time vector
 % Form a signal containing a 50 Hz sinusoid 
 % of amplitude 0.7 and a 120 Hz sinusoid of amplitude 1.
 S = 0.4 + 0.7*sin(2*pi*50*t) + sin(2*pi*120*t) ;
 %Corrupt the signal with zero-mean white noise with a variance of 4.
 X = S + 2*randn(size(t));
 % Plot the noisy signal in the time domain. It is difficult to identify 
 % the frequency components by looking at the signal X(t).
 plot(1000*t(1:50),X(1:50))
 title('Signal Corrupted with Zero-Mean Random Noise')
 xlabel('t (milliseconds)')
 ylabel('X(t)')
 %%Compute the Fourier transform of the signal.
 Y = fft(X);
 % Compute the two-sided spectrum P2. Then compute the single-sided 
 % spectrum P1 based on P2 and the even-valued signal length L.
 P2 = abs(Y/L);
 P1 = P2(1:L/2+1);
 P1(2:end-1) = 2*P1(2:end-1);
 % Define the frequency domain f and plot the single-sided amplitude 
 % spectrum P1. The amplitudes are not exactly at 0.7 and 1, 
 % as expected, because of the added noise. 
 % On average, longer signals produce better frequency approximations.
 f = Fs*(0:(L/2))/L;
 %%L,M,H bands
 %     start & stop frequency defenition of bands
 fL = [  30,  120 ]; %fL steht für die niedrigen Frequenzen. Hier werden die Eckfrequenzen definiert.
 fM = [  80,  250 ]; %fM steht für die mittleren Frequenzen. Hier werden die Eckfrequenzen definiert.
 fH = [ 200, 1000 ]; %fH steht für die hohen Frequenzen. Hier werden die Eckfrequenzen definiert.
 % indices of bands
 LI = find((f>=fL(1))&(f<=fL(2))); % Die Funktion "find" listet  alle 0er und 1er-Elemente
 MI = find((f>=fM(1))&(f<=fM(2)));
 HI = find((f>=fH(1))&(f<=fH(2)));
 % mean values of bands
 ML = mean(P1(LI));
 MM = mean(P1(MI));
 MH = mean(P1(HI));

Answer 1

[Image Analyst]

You can just put them on their own lines:

P1(LI)
P1(MI)
P1(HI)

You do know that your frequency bands overlap, and that your low frequency band does not contain the lowest frequencies, right?

[Denny Muttathil]

@Imgae Analyst Yes i am aware of that. The values for the frequency band were chosen rather randomely. Anyway thanks for helping me. It seems really simple, but i am new to matlab, so it was really confusing for me.