Domain of convolution vs. domain of Fourier transforms

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Discussion Overview

The discussion revolves around the relationship between the domain of convolution and the domain of Fourier transforms, specifically addressing the output signal length resulting from convolution compared to the input signal lengths. The scope includes theoretical aspects of signal processing and mathematical reasoning related to discrete-time signals.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant states that convolving two signals results in a signal length of X+Y-1, while the convolution theorem suggests that the output length should match the input length due to the properties of the Fourier transform.
  • Another participant clarifies that the Fourier transform is unitary for the L² norm, but this does not imply that the output will have compact support.
  • A third participant emphasizes the need to be precise about the convolution theorem, noting that the Discrete Fourier Transform (DFT) assumes periodic signals, leading to circular convolution unless zero-padding is applied to achieve linear convolution.
  • This participant also points out that zero-padding is necessary to avoid artifacts from periodic extensions and that the length of the signals must be at least X+Y-1 for proper linear convolution.
  • The original poster confirms that they are indeed referring to discrete-time signals, which aligns with the assumptions made in the discussion.

Areas of Agreement / Disagreement

Participants express differing views on the implications of the convolution theorem and the properties of the Fourier transform. There is no consensus on the interpretation of the output signal length in relation to the input signals, and the discussion remains unresolved regarding the nuances of these mathematical concepts.

Contextual Notes

Participants note the importance of specifying whether the signals are discrete-time and the implications of periodicity in the context of the DFT. The discussion highlights the need for careful consideration of assumptions when discussing convolution and Fourier transforms.

skynelson
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TL;DR
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!
 

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