Signal Processing: DFT spectrum of sinusoid signals

In summary, the discrete Fourier transform can show two peaks in the spectrum for a basic sinusoidal signal with an integer number of cycles in N samples. The peaks are located at m and N-m, with the latter being equivalent to -m. This is due to the symmetry of the DFT and the conjugate phase relationship between positive and negative frequencies. This can also be demonstrated using FFT software such as Excel.
  • #1
Master1022
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TL;DR Summary
Why does the DTFT of a sinusoidal signal with an integral number of cycles in ## N ## samples yield a spectrum with two peaks
Hi,

I was recently reading about the discrete Fourier transform and its application to a basic sinusoidal signal. If we know that it has an integer number of cycles in ## N ## samples (and thus no leakage), why would there be two peaks in the spectrum: one at ## m ## and another at ## N - m## (as shown in the image below)? I am guessing that ## m ## is the number of samples per cycle. It makes sense that there is a peak at ## m ##, but it isn't immediately clear why there should be one at ## N - m##. I can see the apparent symmetry of the term, but cannot intuitively reason why it should be present. I would appreciate any help or guidance as to why this is the case.

Screen Shot 2021-03-24 at 8.55.03 AM.png
By looking at the mathematical form of the DFT, we have:
[tex] F(n) = \sum_{k = 0}^{N - 1} f[k] e^{-j\frac{2\pi n}{N} k} [/tex]

so would the following be correct?
[tex] F(m) = \sum_{k = 0}^{N - 1} f[k] e^{-j\frac{2\pi m}{N} k} [/tex]
[tex] F(N - m) = \sum_{k = 0}^{N - 1} f[k] e^{-j\frac{2\pi (N - m)}{N} k} = \sum_{k = 0}^{N - 1} f[k] e^{-j2 \pi k(1 - \frac{m}{N})} [/tex]
[tex] \rightarrow \sum_{k = 0}^{N - 1} f[k] e^{j2 \pi k \frac{m}{N}} e^{-j2 \pi k} \rightarrow \sum_{k = 0}^{N - 1} f[k] e^{j2 \pi k \frac{m}{N}} [/tex]
because ## e^{-j2 \pi k} = 1 ## for an integer ## k ##. Then somehow due to symmetry, this causes the peak at ## N - m## ( I am not really sure on the exact logic for this last part).

Thanks in advance.
 
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  • #2
What you plotted is the absolute value of the complex FT of ##\displaystyle {\sin mx = {1\over 2i} \Bigl (e^{imx} - e^{-imx}\Bigr )} ##.
You get one peak at m and one at -m . That last one shows up at ## N - m ##

Example: 16 samples of sin(x)
1616597463812.png
Excel | Data | Data Analysis | Fourier transform

1616597601356.png
##\quad##
1616597524545.png
If you have Excel ( ?:) :smile: ) or some other (F)FT software, play with it ! It's fun !

##\ ##
 
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  • #3
Master1022 said:
It makes sense that there is a peak at m, but it isn't immediately clear why there should be one at N−m. I can see the apparent symmetry of the term, but cannot intuitively reason why it should be present.
You are analysing the time window from 0 to 2π = N.
Consider instead shifting the time window to be -π to +π.
The second peak will then have the negative frequency, -m.
You cannot separate the positive and negative frequencies, but they will have conjugate phase.
Reversing the order of samples in time is equivalent to taking the conjugate of the phase in the frequency domain.
 
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Thank you very much @BvU and @Baluncore for your replies! Yes that is correct, I usually just plot the magnitude of the spectrum to avoid worrying about the +- signs...

Does the ## N - m ## peak also have to do with the periodic nature of the DFT?

Also, thanks for the heads up about Excel @BvU - I didn't know it was possible to do that there. Will definitely go give it a go!
 
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1. What is the DFT spectrum of a sinusoid signal?

The DFT spectrum of a sinusoid signal is a plot of the amplitude and phase of the signal components at different frequencies. It shows the frequency content of the signal and can be used to analyze and manipulate the signal.

2. How is the DFT spectrum of a sinusoid signal calculated?

The DFT spectrum of a sinusoid signal is calculated using the Discrete Fourier Transform (DFT) algorithm. This algorithm involves breaking down the signal into its component frequencies and calculating the amplitude and phase at each frequency using complex numbers.

3. What is the significance of the DFT spectrum in signal processing?

The DFT spectrum is significant in signal processing because it allows us to analyze the frequency content of a signal. This is important in applications such as filtering, noise reduction, and compression, where different frequency components of a signal may need to be manipulated or removed.

4. How does the DFT spectrum of a sinusoid signal differ from other types of signals?

The DFT spectrum of a sinusoid signal differs from other types of signals in that it only contains a single frequency component. Other types of signals, such as square waves or noise signals, may contain multiple frequency components, resulting in a more complex DFT spectrum.

5. Can the DFT spectrum of a sinusoid signal be used for signal reconstruction?

Yes, the DFT spectrum of a sinusoid signal can be used for signal reconstruction. By taking the inverse DFT of the spectrum, we can reconstruct the original signal with minimal loss of information. However, the accuracy of the reconstruction depends on the sampling rate and the number of frequency components included in the DFT spectrum.

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