Fourier coefficients of convolution

In summary, Fourier coefficients of convolution describe how the convolution of two functions can be analyzed using their Fourier transforms. The convolution theorem states that the Fourier transform of a convolution of two functions equals the product of their individual Fourier transforms. This relationship allows for the simplification of complex operations in signal processing, as it transforms convolution in the time domain into multiplication in the frequency domain, facilitating easier analysis and computation.
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TL;DR Summary
I'm trying to verify to myself that the Fourier coefficients of a convolution are the products of the coefficients of the convoluted functions, but I get stuck.
Let ##h(x)=(f*g)(x)=\frac1{2\pi}\int_{-\pi}^\pi f(x-y)g(y)dy## be the convolution. Then its Fourier coefficients are given by $$ {1\over2\pi}\int_{-\pi}^\pi (f*g)(x)e^{-inx}dx={1\over4\pi^2}\int_{-\pi}^\pi\left(\int_{-\pi}^\pi f(x-y)g(y)dy\right)e^{-inx}\ dx\ . $$
Changing the order of integration, we get $${1\over4\pi^2}\int_{-\pi}^\pi g(y) \left(\int_{-\pi}^\pi f(x-y)e^{-inx} dx\right)\,dy\ .$$ Now here I'd like to do the substitution ##t=x-y## in the inner integral, but this makes the limits of integration depend on ##y##, which I do not want. How can I go about this issue?

EDIT: I know that ##h(t)## is periodic with period ##2\pi##. I don't know if this can be helpful in any way.
 
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I guess, after the substitution ##t=x-y##, we get $${1\over4\pi^2}\int_{-\pi}^\pi g(y)e^{-iny} \left(\int_{-\pi-y}^{\pi-y} f(t)e^{-int} dt\right)dy .$$ So here the interval ##[-\pi-y,\pi-y]## is still an interval over a whole period, so we can safely replace it ##[-\pi,\pi]## since ##y## is kept constant in the inner integral anyway. Therefor we get $${1\over4\pi^2}\int_{-\pi}^\pi g(y) e^{-iny}\left(\int_{-\pi}^\pi f(t) e^{-int} dt\right)\,dy\ ,$$ which proves the result.
 
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FAQ: Fourier coefficients of convolution

What are Fourier coefficients of convolution?

Fourier coefficients of convolution refer to the coefficients obtained when taking the Fourier transform of the convolution of two functions. If two functions \( f \) and \( g \) have Fourier transforms \( \hat{f} \) and \( \hat{g} \), then the Fourier transform of their convolution \( (f * g)(t) = \int f(t - \tau) g(\tau) d\tau \) is given by \( \hat{f} \hat{g} \), where \( * \) denotes convolution.

How are Fourier coefficients of convolution calculated?

To calculate the Fourier coefficients of convolution, first compute the Fourier transforms of the individual functions. Then, multiply these transforms together. Finally, take the inverse Fourier transform of the product to obtain the convolution in the original domain. The relationship can be summarized as \( \mathcal{F}(f * g) = \mathcal{F}(f) \cdot \mathcal{F}(g) \).

What is the significance of Fourier coefficients of convolution in signal processing?

In signal processing, Fourier coefficients of convolution are significant because they allow for the analysis and filtering of signals. By convolving a signal with a filter (represented as a function), the Fourier coefficients provide insight into how the filter modifies the frequency components of the signal, enabling tasks such as noise reduction and feature extraction.

Can the Fourier coefficients of convolution be applied to discrete signals?

Yes, the Fourier coefficients of convolution can be applied to discrete signals using the Discrete Fourier Transform (DFT). In this case, the convolution of two discrete sequences corresponds to multiplying their DFTs, which is particularly useful in digital signal processing for efficient computation using algorithms like the Fast Fourier Transform (FFT).

What are some common applications of Fourier coefficients of convolution?

Common applications of Fourier coefficients of convolution include image processing (such as blurring and sharpening), audio signal processing (like equalization and filtering), and solving differential equations in engineering and physics. They are also used in machine learning for feature extraction and in various scientific fields for analyzing periodic phenomena.

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