Understanding Fourier Transforms for Sound Spectroscopy

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In summary, the conversation discussed the use of discrete Fourier transform to draw a sound spectrum. The main question was about the role of the imaginary part and how to graph it. It was explained that the imaginary part represents the 'sin' part of the sound signal and is not mystical but just part of the math. When graphing a power spectrum, both the real and imaginary parts are taken into account.
  • #1
Ana09
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Homework Statement


Hi, I am trying to draw a sound spectrum using the discrete Fourier transform, but there are still a few things I do not understand:
- what does the "imaginary part" really represent? Does it exist physically in the sound? Is it a part of the sound signal that we don't hear or something? Or is it just part of the math?
- How do I graph the imaginary part?


Homework Equations



The DFT's equation.

The Attempt at a Solution



Maybe using argan's diagram? But all the spectra I have seen are plotted in regular cartesian axes...and I have also read that the imaginary part is not represented, as it is just the same as the real part..

 
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  • #2
It depends on the conventions of your DFT. If have something like your signal is equal to the sum of terms like A_n*exp(2*pi*i*n*k/N), if the signal is real, you will have another term like A_{-n}*exp(2*pi*i*(-n)*k/N). The two are complex conjugates (since you will find A_n and A_{-n) are complex conjugates) and sum the give you a real number (in your notation it may be N-n instead of -n. Same thing.) If you expand exp(i*t)=cos(t)+i*sin(t) and combine the two terms you will see that that the real parts correspond to the 'cos' part of the signal and the imaginary parts to the 'sin' part. So there's nothing mystical about it, there's no i's left in the end. When you are graphing a power spectrum what you are graphing is Re(A_n)^2+Im(A_n)^2. So you don't throw away the imaginary parts. You can throw away A_{-n}, since you can reconstruct it knowing A_n.
 

1. What is a Fourier Transform?

A Fourier Transform is a mathematical tool used to transform a signal from the time domain to the frequency domain. It decomposes a signal into its individual frequency components, making it easier to analyze and understand.

2. How is a Fourier Transform calculated?

A Fourier Transform is calculated by taking a signal and breaking it down into its constituent frequencies using complex numbers and integration techniques. This results in a representation of the signal in the frequency domain, which can then be analyzed using various techniques.

3. What is the purpose of using a Fourier Transform?

The purpose of using a Fourier Transform is to gain a better understanding of a signal by breaking it down into its individual frequency components. This allows for easier analysis and manipulation of signals used in various fields such as engineering, physics, and mathematics.

4. What are some applications of Fourier Transforms?

Fourier Transforms have a wide range of applications, including signal processing, image processing, audio and video compression, and data analysis. They are also used in fields such as physics, engineering, and mathematics to analyze and understand various types of signals.

5. Are there different types of Fourier Transforms?

Yes, there are different types of Fourier Transforms, such as the Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT), and Inverse Fourier Transform (IFT). Each type has its own specific use and applications, but they all follow the same general principle of transforming signals from the time domain to the frequency domain.

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