Discrete Fourier Transform (Part 2 - Windowing)

Discrete Fourier Transform (Part 2 - Windowing)

The Discrete Fourier Transform (DFT) gives us a representation of the frequency content of a whole signal, but what if we want to look at how that frequency content changes over time? To achieve that, we need to using windowing to extract a small portion of the signal at a time and then we can compute the DFT of each portion. Unfortunately, the process of applying a window to a signal in this way can introduce artefacts into the result of the DFT and in this video I give a brief overview of why this happens and what can be done to minimize the problem.

You can also check out part 1 of this mini-series on the discrete Fourier transform here:
https://youtu.be/ITnPS8HGqLo

An another video that might be of interest on Gradient Descent optimization:
https://youtu.be/BjkmFVv4ccw

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As with all of my videos, I show the code that I have developed to solve the particular problems that I need to solve, with the hope that it may provide sufficient background and inspiration to allow you to go on to develop your own code if you wish to. My code may not be the most appropriate solution to the problem you are trying to solve, so I urge you to consider the problem carefully and decide for yourself on the most appropriate solution. I make every effort to ensure that my code works as it should and is free of bugs, but of course I cannot provide any guarantees. If you use my code as shown, I strongly encourage you to make sure that you test it thoroughly to ensure that it works as you need it to. If you find a bug, do please let me know in the comments!

Видео Discrete Fourier Transform (Part 2 - Windowing) канала QuantitativeBytes