What is Windowing in Signal Processing?
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- เผยแพร่เมื่อ 9 ก.พ. 2025
- Explains the role of Windowing in signal processing, starting with an example of basic audio compression.
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Using your videos to review signal processing … Thanks, really simplifies the process.
That's great to hear! I'm glad you're finding them helpful.
I always watch your videos they honestly amazing and the way you demonstrate is so great, however I am looking forward to watching anything you could do it about FBMC especially its channel estimation
I'm so glad you like the channel. And thanks for the topic suggestion. I'll add it to my "to do" list. In the mean time, you might like to watch the following video, which explains the effect of the pulse shaping (D-to-A) filter in OFDM, and shows that if you choose the filter (g(t)) to be a sinc function, then you can get rid of the sidelobes in OFDM - which is basically just what FBMC is doing: "How are OFDM Sub Carrier Spacing and Time Samples Related?" th-cam.com/video/knjeXo3VZvc/w-d-xo.html
Always enjoy watching your videos Iain... learn something every time. Rgds, Howard W.
Glad to hear it! Thanks for letting me know. It's always great to hear from viewers who like the content.
Hello. Please consider doing videos on FIR and IIR filter design. The various design techniques and trade offs.
Great suggestion! That's on my list to cover.
Thanks for your video it show some use of Windowing functions but you forget to tell why it's so important to use windowing as a prestep before FFT, DFT transform. All kids knows it is forbidden to divide a number with zero and when they enter High school they learn about L'Hopitals Rule that in some situations it's ok to do that. In universities you learn about FFT and DFT and their inverse functions but at the same time FFT and DFT comes with the requirement, that the signal has the same start and end values, The window function can manipulate the time serie to fulfill that requirement. People are using FFT as gunslingers and do not think why they get weird results. Fortunately your time series has the same start and end value.
Good point. Here's my video on that aspect: "Why is Windowing Needed in Digital Signal Processing?" th-cam.com/video/PQc9DXdV0Xg/w-d-xo.html
@@iain_explains I just saw it, but still you do not say that the DFT treat the windowing signal as the signal where repeating it self to infinity and therefore it is important with same start and end values. You example for P = NT = 8 fulfill the requirement. When you take the window length less than NT, you will get a messy spectrum because your square windowing function does not force the time serie to have same start and end values. Here comes the real world problem when you have a noisy time serie you can't find the periodicity of the serie to select your time window length, it will be quite often too small or too long and that will give the engineer a false picture of the real spectrum. Only very lucky engineers can select the right length 🙂
@@SR-ml4dnSo how can we select the right window, let's say as the oscilloscope does ?
Can you make a video on how to design FIR or IIR filter for music application ?
Thank you for these great videos. How are these windows and their duration chosen when one examines actual occupied band of a communication system? If we are given 20Mhz bandwidth for 5g, and have a freedom of choosing the window duration, and signal is dynamic in time, how is judgment about actual physical presence of spectrum at a given time made?
When signals are random (or "dynamic in time" as you put it), we look at the "power spectral density". It can be thought of as an 'average Fourier transform energy". Here's a video on that: "What is Power Spectral Density (PSD)?" th-cam.com/video/DoSLMEEo1Y0/w-d-xo.html
You said when there are 2 frequencies, theiy could canceling out. I just know that canceling from the time domain - but that is here also possible in the frequency domain?
Do you mean after the convolution of the rectangular and ther 2-frequencies-containing-pattern in the freq.-domain?
Actually, I didn't say "cancelled out", I said "swamped". What I meant was, that if the amplitude of the second frequency component is smaller than the amplitude of the side-lobe of the main frequency component, then it will be hard to decide whether the second frequency component is actually in the signal, or not.
Hi professor.
Can i have you email i have questions regarding how ofdm and ofdma uses mimo and mu-mimo and how they use their subcarrier, i watched your videos but they didn't answer my questions .
Thank you