Why complex discrete Fourier transform?

In summary, the discussion on why complex discrete Fourier transforms are standard instead of just using the real version includes a hypothetical efficiency argument. However, this argument is not always applicable and there are situations where complex numbers are necessary, such as in representing the response of rotating machines. While there may be some computational efficiency in using the real version, the difference is not significant and the same algorithm can be used for both.
  • #1
Jamal26
4
0
I've been trying to figure out why it's standard to use complex discrete Fourier transforms instead of just the real version. It's discussed a bit here.

http://dsp.stackexchange.com/questions/1406/real-discrete-fourier-transform

As far as I can tell there's a hypothetical efficiency argument. Am I wrong? I notice that a lot of computer algorithms are using the real version. For instance ffmpeg tends to use the real version as far as I can tell.

It seems to me that before computers, complex numbers were used to aid in hand computation. For instance it's easier to view sinusoidal functions as a power function of e than it is to calculate sines and cosines when you're doing it by hand.

Am I completely wrong about this analysis? In the case of Fourier transforms do I get missing data or have reduced accuracy when I use the real version instead of the complex version?

Thanks.
 
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  • #2
THe stackexchange answer is pretty good. The bottom line is, there are some irritating sign-changes between ##A \cos\omega t + B\sin\omega t## and the real part of ##(A + iB)e^{i\omega t}##. The math works out tidier in the general case where both the data and its transform are complex.

FWIW The stackexchange comment
Of course, no physical system has complex-values signals going in and coming out
is not always true. If you consder the behaviour of a rotating machine, the simple way to represent its response in the time domain is using complex variables, in a similar way that electrical engineers use "phasors".

I think the "computational efficiency argument" is entirely hypothetical. if you want efficiency (and numerical accuracy) it is not usually a good plan to make a naive translation of math formulas using complex numbers, into computer code using complex variables. But aside from that, you can use exactly the same algorithm for doing an FFT of 2n real variables or n complex variables, apart from a different interpretation of the order of the input and output data which has no effect on the computing time or cost.
 

What is a complex discrete Fourier transform?

A complex discrete Fourier transform (DFT) is a mathematical technique that converts a discrete signal into its frequency representation. It decomposes a signal into its constituent frequencies and their corresponding amplitudes. The resulting representation is complex, with both real and imaginary components.

Why is the complex DFT used?

The complex DFT is used because it allows for a more accurate representation of signals with both real and imaginary components. It is also more efficient in terms of computation compared to other methods of frequency analysis.

What are the applications of complex DFT?

The complex DFT has various applications in signal processing, image processing, data compression, and communication systems. It is also widely used in scientific and engineering fields for analyzing and manipulating data in the frequency domain.

How is the complex DFT different from the discrete Fourier transform?

The complex DFT differs from the discrete Fourier transform (DFT) in that it allows for a complex representation of the signal, while the DFT only produces a real representation. This means that the complex DFT can capture both the magnitude and phase information of a signal, while the DFT only captures the magnitude.

What are the limitations of the complex DFT?

The complex DFT has limitations in terms of its frequency resolution and the size of the input signal. It also assumes that the signal is periodic, which may not always be the case in real-world applications. Additionally, the complex DFT is not suitable for analyzing non-stationary signals that change over time.

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