Timeline for How come FFT window give half size frequency bands when filter can seems to have an infinite precision?
Current License: CC BY-SA 4.0
9 events
| when toggle format | what | by | license | comment | |
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| Apr 10, 2020 at 15:10 | comment | added | JSmith | thanks for the advice. I'll do that | |
| Apr 10, 2020 at 15:09 | comment | added | Dan Boschen | @JSmith download Octave (free) and play with this stuff directly and it will start to make more and more sense. Stick with it! Spend a LOT of time looking at correlation which is the sum of products and what happens to signals and noise when you sum (or average). This is the basis of MANY signal processing concepts including the Fourier Transform, Laplace Transform, filtering, receivers, Viterbi decoding, etc etc etc you will see that sum of products (or multipy and integrate in the analog domain) show up again and again so I recommend you get your head around that. | |
| Apr 10, 2020 at 15:08 | comment | added | JSmith |
Haha I had a look and honestly this seems like a foreign language to me . Started reading The scientist and enginer's Guide to Digital Signal Processing of Steven W . Smith but honestly even trying to understand step by step is not as fluent as I would like. Anyway great explanation of yours thanks again .
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| Apr 10, 2020 at 15:04 | comment | added | Dan Boschen | @JSmith it may help you to realize (if you didn't) that the expression $Ke^{j\theta}$ is simply a vector with magnitude $K$ and angle $\theta$ | |
| Apr 10, 2020 at 14:52 | history | edited | Dan Boschen | CC BY-SA 4.0 |
added 130 characters in body
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| Apr 10, 2020 at 14:45 | history | edited | Dan Boschen | CC BY-SA 4.0 |
added 130 characters in body
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| Apr 10, 2020 at 14:32 | vote | accept | JSmith | ||
| Apr 10, 2020 at 14:31 | comment | added | JSmith | I think I'lll be able to understand that in a long time. Many thanks for your answer. I'll surely take a deeper look into it. ;) thanks | |
| Apr 10, 2020 at 14:23 | history | answered | Dan Boschen | CC BY-SA 4.0 |