Discrete Fourier Transform and Hand-waving

In summary, the conversation discusses the inverse discrete Fourier transform (DFT) and the relationship between the amplitudes in the frequency domain and space-domain synthesis. It is explained that the amplitudes for the space-domain synthesis are obtained by multiplying the corresponding frequency domain by 2/N, except for the first and last frequency terms which are multiplied by 1/N. A digression is made on 'spectral densities' but it is not shown how it is connected to the topic. The conversation ends with a detailed explanation of how the real and imaginary parts of the DFT can be obtained and why there is a factor of 2 on some terms and not others.
  • #1
mathman44
207
0
Hi all,

I'm reading the following PDF about the DFT:

http://www.analog.com/static/imported-files/tech_docs/dsp_book_Ch8.pdf

Please see pages 152-153.

So the inverse DFT (frequency to space, x = ...) is given on page 152. Then it is claimed that the amplitudes for the space-domain synthesis (inverse DFT) are different than the frequency domain of a signal (page 153).

In short, the amplitude for the space-domain synthesis is the corresponding frequency domain multiplied by 2/N, except for the zero frequency term and the last frequency term, which are multiplied by 1/N.

A short digression on 'spectral densities' are made, and I really don't see the connection. Why are the different frequency components weighted differently in the space-domain synthesis?

Any help would be great.
Thanks.
 
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  • #2
If we define
$$X(k) = \sum_{n=0}^{N-1} x(n) e^{-i2\pi k n/N}$$
for ##k=0, 1, \ldots, N-1##, and we want to recover ##x(n)## from ##X(k)##, then
$$x(n) = \frac{1}{N} \sum_{k=0}^{N-1} X(k) e^{i2\pi k n/N}$$
Here the ##X(k)## are complex-valued. We can obtain an equivalent expression in terms of the real and imaginary parts of ##X(k)## by writing ##X(k) = R(k) + iI(k)## and simplifying:
$$x(n) = \frac{1}{N} \sum_{k=0}^{N-1} R(k) e^{i2\pi k n/N} + i \frac{1}{N} \sum_{k=0}^{N-1} I(k) e^{i2\pi k n/N}$$
where ##R(k)## and ##I(k)## are real-valued.

Now suppose the original data ##x(n)## is real-valued. Then notice that if we take the complex conjugate of the first equation above, we get
$$X^*(k) = \sum_{n=0}^{N-1} x(n) e^{i2\pi k n/N}$$
Now replace ##k## by ##N-k##:
$$X^*(N-k) = \sum_{n=0}^{N-1} x(n) e^{i2\pi (N-k) n/N} = \sum_{n=0}^{N-1} x(n) e^{-i2\pi k n/N} = X(k)$$
So we have established that ##X^*(N-k) = X(k)##. Now take the real and imaginary parts of this equation. We see that ##R(N-k) = R(k)## and ##-I(N-k) = I(k)##. This allows us to rewrite the two sums involving ##R(k)## and ##I(k)##. I will show how this works for ##R(k)##. You can do something similar for ##I(k)##.
$$\begin{align}
\sum_{k=0}^{N-1} R(k) e^{i2\pi k n/N} &= R(0) + \sum_{k=1}^{N/2 - 1} R(k) e^{i2\pi k n/N} + R(N/2) e^{i\pi n} + \sum_{k=N/2+1}^{N-1} R(k) e^{i2\pi k n/N}\\
&= R(0) + \sum_{k=1}^{N/2 - 1} R(k) e^{i2\pi k n/N} + R(N/2) \cos(\pi n) + \sum_{k=N/2+1}^{N-1}R(N-k)e^{i2\pi kn/N} \\
&= R(0) + \sum_{k=1}^{N/2 - 1} R(k) e^{i2\pi k n/N} + R(N/2) \cos(\pi n) + \sum_{m=1}^{N/2-1} R(m) e^{-i 2\pi mn/N} \\
&= R(0) + \sum_{k=1}^{N/2 - 1} R(k) (e^{i2\pi k n/N} + e^{-i2\pi k n/N}) + R(N/2)\cos(\pi n) \\
&= R(0) + 2 \sum_{k=1}^{N/2 - 1} R(k)\cos(2\pi k n/N) + R(N/2) \cos(\pi n)
\end{align}$$
This shows why there is a factor of 2 on all of the terms from ##k=1, 2, \ldots N/2-1## but not on the ##k=0## and ##k=N/2## terms.

I have used the following facts above: for the ##k=0## term,
$$e^{i2\pi k n/N} = e^{i2\pi 0 n/N} = e^{0} = 1$$
For the ##k=N/2## term:
$$e^{i2\pi k n/N} = e^{i2\pi (N/2) n/N} = e^{i \pi n} = \cos(\pi n) + i\sin(\pi n) = \cos(\pi n) + 0 = \cos(\pi n)$$
The RHS of this last equation also equals ##(-1)^n## but there was no need to use that fact here.
 
Last edited:
  • #3
Whoa... thanks, that's absolutely perfect.
 

FAQ: Discrete Fourier Transform and Hand-waving

1. What is the Discrete Fourier Transform (DFT)?

The Discrete Fourier Transform is a mathematical technique that allows us to analyze signals in the frequency domain. It is often used in signal processing and data analysis to identify periodic components and their associated frequencies.

2. How does the DFT differ from the Fast Fourier Transform (FFT)?

The DFT is a general mathematical algorithm for computing the Fourier Transform of a discrete signal. The FFT is an efficient implementation of the DFT that takes advantage of symmetries and redundancy in the calculations. In other words, the FFT is a specific algorithm for computing the DFT.

3. What is "hand-waving" in the context of the DFT?

"Hand-waving" in the context of the DFT refers to a simplified or intuitive explanation of the mathematical concepts behind the DFT. It may involve using visual aids or analogies to help understand the underlying principles.

4. Can the DFT be used for any type of signal?

Yes, the DFT is a general mathematical technique that can be applied to any discrete signal. However, the signal must be finite and have a discrete, evenly spaced time domain.

5. What are the practical applications of the DFT?

The DFT has many practical applications, including signal processing, data analysis, image processing, and audio compression. It is also used in fields such as engineering, physics, and mathematics to analyze and understand periodic phenomena.

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