Need help understanding Fourier transform in Hz vs radians

In summary, the Fourier transform can be defined using either angular frequency or frequency in Hz as the argument in the exponential. This results in different scaling factors for the inverse transform, but both conventions give the same result. In mathematics, the normalization constant is sometimes divided evenly between the forward and reverse transforms, while in physics and engineering, it is typically chosen to give the proper power spectral density.
  • #1
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Hi guys,

I'm having some issues understanding something about the Fourier transform. In my first signals and systems class we used the angular frequency omega. Doing it like that you end up with a weighing factor or 1/(2pi) when you take the transform. Now in the dsp class I am taking now we are using the frequency in Hz.

The thing I don't get is how can the amplitude in one frequency be different than in another for the same signal. I also read about another way of doing it where in both directions you multiply it by 1/sqrt(2pi), helping to preserve duality.

Is the frequency transform basically different based on how it is interpreted? Can someone help me out here, I don't know exactly what I am confused about but I don't see how it can just be arbitrarily defined and have different amplitudes for what is apparently the same thing just in a different frequency?

For example a sine wave has an amplitude of 1. So it would seem reasonable that in the Fourier transform it would have an impulse of 1 at the correct frequency. But if you use radians for the Fourier transform, it has a different amplitude!
 
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  • #2
There shouldn't be any fundamental difference. When you take the FT in Hz, the argument of the exponential contains a factor of 2*pi, that is, exp(i*2*pi*f*t). Using angular freqency, the argument is exp(i*omega*t). You deal with real signals in your engineering classes, so the normalizing factor will be chosen to give the proper power spectral density (in Watts/Hz, e.g.) given a signal amplitude in volts.
 
  • #3
So the Fourier Transform is still the Fourier Transform, regardless of what you scale it by? This seems weird because it would mean that if you tell someone, the FT of this is X, it could mean anything unless you give the scaling factor.
 
  • #4
If you define the FT as

[tex]G(f)=\int_{-\infty}^{\infty} g(t)exp(-i2\pi ft) dt [/tex],

then the inverse transform is

[tex]g(t)=\int_{-\infty}^{\infty}G(f)exp(i2\pi ft) df [/tex].

If you change variables to

[tex]\omega=2\pi f[/tex]

you necessarily get a factor of [tex]1/(2\pi)[/tex] in front of the inverse transform:

[tex]g(t)=\frac{1}{2\pi}\int_{-\infty}^{\infty}G(\omega)exp(i\omega t) d\omega [/tex].

In this case there is no difference in the amplitudes, both inverse expressions give the same thing. Notice that forward and reverse FT's are asymmetric in the latter case.

In mathematics the normalization constant is sometimes apportioned evenly between fwd and reverse transforms (using angular frequencies omega) by multiplying each by [tex]1/\sqrt{2\pi}[/tex]. This restores symmetry but changes the normalization. Thus the first two conventions are preferred in physics and engineering.

EDIT: Don't know why Latex put primes and dots next to the differentials...
 

1. What is the Fourier transform in Hz vs radians?

The Fourier transform is a mathematical tool used to decompose a function into its constituent frequencies. This can be represented in two ways: in Hertz (Hz) or in radians per second. Hz is a unit of frequency, while radians per second is a unit of angular frequency. Both units measure the number of cycles per second, but radians per second takes into account the full circle of a sine wave, while Hz only measures the number of peaks in a wave.

2. What is the difference between Hz and radians in the Fourier transform?

The main difference between Hz and radians is the way they measure frequency. Hz is a linear unit of frequency, while radians is an angular unit. This means that Hz only measures the number of cycles per second, while radians takes into account the full circle of a sine wave. In practical terms, this means that Hz is used for signals with a constant amplitude, while radians is used for signals with varying amplitude.

3. How is the Fourier transform used in signal processing?

The Fourier transform is a powerful tool in signal processing as it allows us to analyze signals in the frequency domain. This means that we can break down a signal into its constituent frequencies, making it easier to identify and filter out unwanted noise. The Fourier transform is used in a wide range of applications, such as audio and image processing, communication systems, and medical imaging.

4. What are the advantages of using radians in the Fourier transform?

One advantage of using radians in the Fourier transform is its ability to handle signals with varying amplitude. Since radians takes into account the full circle of a sine wave, it is better suited for signals that are not constant over time. Additionally, using radians can simplify certain mathematical operations, making the analysis of signals more efficient.

5. How can I visualize the Fourier transform in Hz vs radians?

One way to visualize the Fourier transform in Hz vs radians is to plot the frequency spectrum of a signal. In this plot, the x-axis represents frequency, with Hz on a linear scale and radians on a logarithmic scale. This allows you to see the distribution of frequencies in the signal and compare the results in both units. Additionally, many signal processing software packages have built-in tools for visualizing the Fourier transform in both Hz and radians.

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