Domain of convolution vs. domain of Fourier transforms

In summary, the convolution theorem states that convolving two signals, g and h, of lengths X and Y respectively, results in a signal with length X+Y-1. However, the Fourier transform is unitary, meaning the output signal will have the same length as the input signal for that operation. This is because the Discrete Fourier Transform (DFT) assumes that the input signal is one period of an infinitely long periodic signal. To compute the linear convolution, both signals must be zero-padded to a length of at least X+Y-1.
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skynelson
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Convolving two signals, g and h, of lengths X and Y respectively, results in a signal with length X+Y-1. How can the length of an output signal of convolution be different from the input signals , given the contents of the Convolution Theorem? Thank you!
Convolving two signals, g and h, of lengths X and Y respectively, results in a signal with length X+Y-1. But through convolution theorem, g*h = F^{-1}{ F{g} F{h} }, where F and F^{-1} is the Fourier transform and its inverse, respectively. The Fourier transform is unitary, so the output signal is the same length as the input signal for that operation. The prescribed pointwise multiplication also requires signals of the same length (I believe the smaller will be padded to match the larger if needed).

How can the length of an output signal of convolution be different from the input signals , due to the Convolution Theorem?
 
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Fourier is unitary for the ##L^2## norm, not for the length of the support. Consider this, the Fourier transform of a function of compact support will not have compact support.
 
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You aren't being careful enough with your statement of the convolution theorem. Recall that the Discrete Fourier Transform (DFT) assumes that the input signal is one period of an infinitely long periodic signal. So using the DFT computes the so-called circular convolution of the two periodic signals. If you want the linear convolution (which has the length you are stating) then you will need to zero-pad both signals so that you do not get any of the artifacts from the periodic extension of the signals. At the end of the day you should find that in order to compute the linear convolution via the DFT, you need to zero pad both signals so that they each have length## \geq X+Y-1##.

jason

EDIT: I am obviously assuming you are dealing with discrete-time signals because otherwise your length question doesn't make sense. If my assumption is incorrect then your question might not make sense. In any case, it is usually best to give such details in your question.
 
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Thank you for your helpful reply. Indeed, this clears things up. Yes, I am referring to a discrete transform. Much appreciated!
 

Related to Domain of convolution vs. domain of Fourier transforms

1. What is the difference between the domain of convolution and the domain of Fourier transforms?

The domain of convolution refers to the set of values over which the convolution operation is performed. This can include both time and frequency domains. On the other hand, the domain of Fourier transforms refers to the set of values over which the Fourier transform is calculated, which is typically in the frequency domain.

2. Can the domain of convolution and the domain of Fourier transforms be the same?

Yes, in some cases the domain of convolution and the domain of Fourier transforms can be the same. This occurs when the signals being convolved are both in the frequency domain, or both in the time domain.

3. How does the choice of domain affect the results of convolution and Fourier transforms?

The choice of domain can greatly affect the results of convolution and Fourier transforms. For example, convolving two signals in the time domain will result in a time-domain output, while convolving two signals in the frequency domain will result in a frequency-domain output. Similarly, taking the Fourier transform of a signal in the time domain will result in a frequency-domain representation of that signal.

4. Can the domain of convolution and the domain of Fourier transforms be converted from one to the other?

Yes, the domain of convolution and the domain of Fourier transforms can be converted from one to the other using mathematical operations. For example, the convolution of two signals in the time domain can be converted to the frequency domain by taking the Fourier transform of both signals and then multiplying them together.

5. In what situations would it be more appropriate to use the domain of convolution versus the domain of Fourier transforms?

The choice of domain depends on the specific problem at hand. In general, the domain of convolution is used when dealing with signals that are changing over time, such as audio signals. The domain of Fourier transforms is commonly used in signal processing and image processing applications, where it is useful for analyzing the frequency components of a signal or image.

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