How to use the FFT like a pro, 3 essential signal prep tips
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- čas přidán 11. 07. 2024
- Unsure how to use the FFT to get meaningful results from your data? Join me as I unveil 3 crucial signal preparation tips to ensure accurate frequency analysis.
In this video, you'll discover:
1. How to find the perfect sampling rate to avoid aliasing and capture all the frequencies in your signal.
2. How low-pass filters prevent high-frequency noise from distorting your signal and messing up your FFT results.
3. How windowing functions smooth out signal edges and minimize spectral leakage for cleaner FFT output.
Stop feeding garbage into your FFT and start getting the insights you deserve!
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the-fourier-transform.teachab...
00:00 Introduction
00:31 Ident
00:50 Tip 1: Set the optimum sampling rate
02:38 Tip 2: Use an antialiasing filter
03:51 Tip 3: Use a windowing function
07:16 Master the Fourier Transform
![Denoising Data with FFT [Python]](http://i.ytimg.com/vi/s2K1JfNR7Sc/mqdefault.jpg)
Thank you. Concise and accessible, yet still gives a surprisingly thorough introduction to sampling issues.
Glad it was helpful. Two more videos are coming in this series which I'll be releasing over the next few weeks. However, you can see them already as I have linked to them on the end screen.
im not an engineer, im learning EEG analisis and ur vudeos have been so helpfull, thanks and greettings from CDMX
I did not expect this video in my feed, but it's exactly what I need
Glad to have been of service.
OMG you are INCREDIBLE. what a treasure!
You're always so nice about my stuff! Thanks.
Welcome back sir! Great video as always!
Just ordered the book.
Brilliant , brilliant brilliant 👏.
Thanks so much.
Very nicely explained.
Really happy to have helped.
really good
Great video! Very entertaining
Glad you enjoyed it!
This is fantastic, thanks!
You're very welcome!
Great video. I especially like the part about the windowing function. You explain why it's needed really well. Hanning window is OK when you are dealing with low dynamic range. Like 10 to 20 dB. It falls short when you increase the dynamic range above that. I recommend the Kaiser window. It has somewhat narrow main lobes and is adjustable. You can even go above 100 dB of dynamic range. You can select the correct value for the Kaiser window based on how much dynamic range you need. For audio 50-60 dB is sufficient.
Great tip. Thanks.
The G.O.A.T
Thanks.
Thank you ❤
You're welcome 😊
The boss is back!
real life sheldon cooper feels like hosting fun with flags but its actually really fun to watch
give some examples of the power of the fourier transform, always show applications
Hi Mark could you please help, I follow what you are saying in the video, but for applications of the FFT, typically the algorithm does not ask for the Fs of the signal when I implement it in MATLAB or Python. At most it allows for me to specify the size if I want to pad it up to a different length, but it doesn’t need the Fs. I know how to scale the axis when it gives me an output, but how does it know what frequencies to test in the signal if I don’t feed it the sample rate? For example, If I have a function with frequency content up to say 10 MHz and I only have 10,000 data points with a sampling rate of 30 MHz, the FFT in programming doesn’t explicitly ask for the sampling rate, so wouldn’t it only check frequencies up to 10 kHz for the 10,000 points? Caveat that the result looks correct without the giving the sample rate. Why is this?
The FFT algorithm itself will not actually tell you the frequency of any of the sinusoids in your signal. It doesn't care about it. As far as it is concerned, it sees the frequency as an index which ranges from 0 to your FFT size rather than what we would like to see as a number in Hz. In the same way, the FFT algorithm won't directly tell you the Magnitude or Phase of each sinusoid. It only gives you the cosine (real) and sine (imaginary) components for each sinusoid in the signal. If you want to know the Frequency, Magnitude or Phase, you have to work it out for yourself from the list of complex numbers. In your example, you are sampling your signal at 3 times the highest frequency which is great. You won't miss out on any data. To work out the actual frequency of each item in the list, you need to take the position of the item (which will range from 0 to 9,999) and divide it by the number of samples in your FFT (10,000), then multiply the result by 30Mhz. This will give you the frequency in Hz.
Just an observation: 10,000 samples is not a power of 2. If the FFT is running without giving you an error message, then MATLAB is doing something to your signal to make the number of samples a power of 2. Otherwise, the FFT cannot run. The shape of your output may well look right. However, check the actual sample values. They will be a scaled version of the real frequency of each sinusoid unless MATLAB is doing something really clever and is somehow working out what the sample rate is in some way. (For example, if you are feeding it 2 columns of data, one containing the timestamp of each sample and the other containing the amplitude).
have you used the FFT on thought?
No I haven't! That's an interesting idea. How would you measure thought?
How do we find the highest frequency present in a signal?
Ahh. That's difficult. You really need to know something about your signal first. How do you know how big something is until you measure it, but the very act of measuring, in this case, has the potential to alter the measurement. It's more a case of deciding which frequency range interests you. For example, if this is an audio signal that you want humans to hear, filter out anything above 20kHz as we can't hear those frequencies anyway.
@@MarkNewmanEducation Thank for your kind reply sir.
How about zero padding the signal with low number of sample ? 🤔
Aha... you're anticipating next week's video. I've linked to it on the end screen.
If you feed your fft algorithm with a sample of size N, the fft by default gives the spectra for only N values of frequencies. But it could give you more. If you want more values, you trick the fft algorithm by using Zero padding (adding zeros to your original sample to increase artificially N without changing the information of the signal). Zero padding does nothing to address the sampling rate issue; it doesn't change the information contained in the signal.
@@keipfar And what if we insert zero between sparse less number of samples before FFT/DFT ?
@@polarizadmaxI am not aware of such techniques. My guess is you will distort the content of the signal. I don't know.