I generally agree with the point of the article ("Fourier transform is not magical").
However saying it is "just" curve fitting with sinusoids fails to mention that, among an infinite number of basis functions, there are some with useful properties, and sinusoids are one such: they are eigenvectors of shift-invariant linear systems (and hence are also eigenvectors of derivative operators).
All quite good examples but I would say that these are quite well known. It’s also missing that there are mitigation strategies for some - for e.g. in vibration analysis it’s typical to look at the Hann windowed data to remove the effect of partial cycles, and it’s common to overlap samples too. Similarly there are other tools like the Cepstrum which help you identify periodic peaks in the spectral data.
I generally agree with the point of the article ("Fourier transform is not magical").
However saying it is "just" curve fitting with sinusoids fails to mention that, among an infinite number of basis functions, there are some with useful properties, and sinusoids are one such: they are eigenvectors of shift-invariant linear systems (and hence are also eigenvectors of derivative operators).
All quite good examples but I would say that these are quite well known. It’s also missing that there are mitigation strategies for some - for e.g. in vibration analysis it’s typical to look at the Hann windowed data to remove the effect of partial cycles, and it’s common to overlap samples too. Similarly there are other tools like the Cepstrum which help you identify periodic peaks in the spectral data.
I made a video about a cool application of the Discrete Fourier Transform regarding color eink Kaleido 3 and manga:
https://youtu.be/Dw2HTJCGMhw?si=Qhgtz5i75v8LwTyi
Learning about Fourier is really interesting in image processing, I'm glad I found a good textbook explaining it.