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As someone who still doesn't understand the Fourier Transform, this explanation doesn't help, nor does the article's. As a complete noob, your explanation shows

As someone who still doesn't understand the Fourier Transform, this explanation doesn't help, nor does the article's. As a complete noob, your explanation shows off what you know but doesn't help newcomers learn it.

deleted 4y ago
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SV_BubbleTime 4y ago
Imagine you have a voice recording. You are looking at it a long squiggly line with no uniformity. Zoom way in. If you go far enough it’ll just look like a curved line. That curved line can be estimated down into a sin wave, or more accurately, a few sin waves that combine to make almost the same wave you have. FFT is a way to take a complex wave and reduce it down to the sin wave components that would all combine to make it up. To practically apply this, your voice recording is very high resolution and has a lot of bits at a high sample rate. If you instead used sin waves to represent the data, it could be almost as good sounding while being a lot less data to store. … Watch a video. It’s something many people need to see to get.
- AwaAwa 4y ago
  Great explanation! I'm finally on the first step of understanding. Now to read more!
  - jaypinho 4y ago
    I once read an explanation of the Fourier transformation as akin to looking at a fully blended smoothie and being able to calculate exactly how much of each ingredient went into it. I don't profess to be a DSP expert whatsoever, but the more familiar I've become with Fourier transformations, the more apt that analogy seems. Once you grasp that all sound is just a large addition problem of many, many sine waves, the ability to distinguish between them to a fairly high degree of fidelity feels almost like magic.
gehwartzen 4y ago
A visual like the one in this article helped make it click for me: https://kinder-chen.medium.com/denoising-data-with-fast-fourier-transform-a81d9f38cc4c https://kinder-chen.medium.com/denoising-data-with-fast-four...
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nomel 4y ago
Give this a try: https://news.ycombinator.com/item?id=11354730 https://news.ycombinator.com/item?id=11354730
tel 4y ago
Apologies, there’s only so much one can do firing explanations into the void. I don’t really mean to show off, but instead to share meanings which I’ve personally had success sharing with others and having them come to better understanding. I can’t hope that it will be what each individual finds most helpful, however. I also have little hope for any “short” resource to accomplish that. I hope you find what you’re looking for.

deleted 4y ago

[deleted]

SV_BubbleTime 4y ago

Imagine you have a voice recording. You are looking at it a long squiggly line with no uniformity. Zoom way in. If you go far enough it’ll just look like a curved line. That curved line can be estimated down into a sin wave, or more accurately, a few sin waves that combine to make almost the same wave you have. FFT is a way to take a complex wave and reduce it down to the sin wave components that would all combine to make it up. To practically apply this, your voice recording is very high resolution and has a lot of bits at a high sample rate. If you instead used sin waves to represent the data, it could be almost as good sounding while being a lot less data to store. … Watch a video. It’s something many people need to see to get.

AwaAwa 4y ago
Great explanation! I'm finally on the first step of understanding. Now to read more!
- jaypinho 4y ago
  I once read an explanation of the Fourier transformation as akin to looking at a fully blended smoothie and being able to calculate exactly how much of each ingredient went into it. I don't profess to be a DSP expert whatsoever, but the more familiar I've become with Fourier transformations, the more apt that analogy seems. Once you grasp that all sound is just a large addition problem of many, many sine waves, the ability to distinguish between them to a fairly high degree of fidelity feels almost like magic.

gehwartzen 4y ago

A visual like the one in this article helped make it click for me: https://kinder-chen.medium.com/denoising-data-with-fast-fourier-transform-a81d9f38cc4c https://kinder-chen.medium.com/denoising-data-with-fast-four...

deleted 4y ago

[deleted]

nomel 4y ago

Give this a try: https://news.ycombinator.com/item?id=11354730 https://news.ycombinator.com/item?id=11354730

tel 4y ago

Apologies, there’s only so much one can do firing explanations into the void. I don’t really mean to show off, but instead to share meanings which I’ve personally had success sharing with others and having them come to better understanding. I can’t hope that it will be what each individual finds most helpful, however. I also have little hope for any “short” resource to accomplish that. I hope you find what you’re looking for.