I know that FFT changes a function in the time domain to one showed in the frequency domain.
However, when I try plotting said graph in the frequency domain, I can only get it to work properly by using the time as X-axis, when it was obviously supposed to be not that, but the frequency.
Also, I can only get the amplitudes to match the ones in the original signal by dividing the y-axis by a certain integer. Why is that?
Here's my code
t=0:0.001:2
x=2*sin(20*pi*t) + sin(100*pi*t)
subplot(2,1,1)
plot(1000*t,x)
grid
xlabel("Time in milliseconds")
ylabel("Signal amplitude")
subplot(2,1,2)
y=fft(x)
plot(1000*t,abs(y))
xlabel("Frequency")
ylabel("Signal amplitude")
and graphs.
Please help =(
The frequency of each values produced by the FFT is linearly related to the index of the output value through:
f(i) = (i-1)*sampling_frequency/N
Where N is the number of FFT points (ie. N=length(y)
). In your case, N=2001
.
One can deduct the sampling frequency from your definition of t
as 1/T where T is the sampling time interval (T=0.001 in your case).
So the sampling frequency is 1000Hz.
Note that since the value of t(i)
is also linearly related to the index i
, through
t(i) = (i-1)*0.001
it is possible (though not necessarilly advised, as this would just obscure your code) to define f = 1000*t*sampling_frequency/N
.
Note that you were missing the sampling_frequency/N
term which correspondingly resulted in tones being shown at the wrong frequency
(from the definition of x
there should be peaks at 10Hz and 50Hz, and the corresponding aliases at 990Hz and 950Hz).
Note that the observed relationship is only approximate, so the following is not a mathematical proof, but merely a intuitive way to visualize the relationship between the time-domain tone amplitudes and the frequency-domain peak values.
Simplifying the problem to a single tone:
x = A*sin(2*pi*f*t)
The approximate amplitude of the corresponding peak could be derived using Parseval's theorem:
In the time domain (the left side of the equation), the expression is approximately equal to 0.5*N*(A^2)
.
In the frequency domain (the right side of the equation), making the following assumptions:
f
and the corresponding aliased frequency sampling_frequency-f
) account for the summation (all other bins being ~0). Note that this typically only holds if the tone frequency is an exact (or near exact) multiple of sampling_frequency/N
.the expression on the right side is approximately equal to 2*(1/N)*abs(X(k))^2
for some value of k
corresponding to the peak at frequency f
.
Putting the two together yields abs(X(k)) ~ 0.5*A*N
. In other words the output amplitude shows a scaling factor of 0.5*N
(or approximately 1000 in your case) with respect to the time-domain amplitude, as you had observed.
The idea still applies with more than one tone (although the negligible spectral leakage assumption eventually breaks down).