What is the significance of the complex Fourier spectrum in signal processing?

In summary, the speaker is confident in their understanding of complex Fourier series but still has questions about certain aspects. They mention using an fft in MATLAB and obtaining a spectrum with complex conjugate symmetry around the real value in the middle. They understand why the real value must not have an imaginary component, but are unsure of its significance. They also have concerns about integrating in the frequency domain and how to handle the DC value and real value in the middle of the spectrum. They welcome any corrections to their understanding.
  • #1
JohnSimpson
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I feel I have a good grasp of the complex Fourier series, but I'm struggling with a few things still.

When I take, say, an fft in MATLAB (with an even number of data points) I obtain a spectrum that looks like this

[DC] [ + freqs ] [Real Valued Number] [-Freqs]

With complex conjugate symmetry around the Real Value in the middle. I understand why this value in the middle cannot have an imaginary component, as it would destroy the symmetry. The value is it's own complex conjugate. But what is this value? Is it the maximum frequency in the spectrum?

In a related question, say I want to integrate in the frequency domain. That would involve diving by the entire signal by [tex]2pif_nj[/tex] where fn is the fundamental frequency times n. How do I then deal with the DC value, and the real value in the middle? It seems to me that for the DC value I would be diving by zero, and for the real value in the middle of the spectrum, I would be making it complex. Corrections to my understanding are appreciated

-John
 
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Related to What is the significance of the complex Fourier spectrum in signal processing?

What is a Complex Fourier Spectrum?

The Complex Fourier Spectrum is a mathematical representation of a signal or function in the frequency domain. It is a way of decomposing a signal into its individual frequency components, allowing for analysis and manipulation of the signal in terms of its frequency content.

How is the Complex Fourier Spectrum calculated?

The Complex Fourier Spectrum is calculated using the Fourier transform, which converts a signal from the time domain to the frequency domain. This involves breaking down the signal into its individual sinusoidal components and determining their amplitudes and phases.

What is the difference between a Complex Fourier Spectrum and a Real Fourier Spectrum?

A Complex Fourier Spectrum includes both amplitude and phase information for each frequency component, while a Real Fourier Spectrum only includes amplitude information. This means that a Complex Fourier Spectrum can represent signals with both real and imaginary components, while a Real Fourier Spectrum can only represent real-valued signals.

What is the significance of the Complex Fourier Spectrum in signal processing?

The Complex Fourier Spectrum is an essential tool in signal processing as it allows for analysis and manipulation of signals in the frequency domain. It is used in a variety of applications, such as filtering, noise reduction, and compression.

Can the Complex Fourier Spectrum be used for non-periodic signals?

Yes, the Complex Fourier Spectrum can be used for non-periodic signals by using a technique called the Fourier transform. This allows for the representation of a signal's frequency content even if the signal is not repeating in a periodic manner.

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