Filter White Noise: Designing FIR Moving Average Filter

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Discussion Overview

The discussion revolves around designing a low-pass filter for Gaussian white noise intended for use with a vibration exciter. Participants explore the implementation of an FIR moving average filter and consider various methods for filtering while maintaining signal integrity and control over specific parameters.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested

Main Points Raised

  • One participant suggests using an FIR moving average filter but is uncertain about the appropriate windowing method.
  • Another participant questions the necessity of filtering high frequencies, noting that the mechanical apparatus may already act as a low-pass filter.
  • Some participants propose using FFT methods for filtering to save CPU resources, suggesting that modern computers can handle such processing without issue.
  • A participant emphasizes the need for control over the sigma of the Gaussian noise after filtering, indicating a requirement for maintaining specific statistical properties of the noise signals.
  • There is a suggestion to generate white noise in the frequency domain and apply amplitude filtering before converting it back to the time domain using an inverse FFT.

Areas of Agreement / Disagreement

Participants express differing views on the necessity of low-pass filtering the high frequencies, with some agreeing that the mechanical apparatus may suffice while others maintain the need for additional filtering. The discussion remains unresolved regarding the best approach to achieve the desired filtering and control over the signal properties.

Contextual Notes

Participants have not fully clarified the assumptions regarding the filtering requirements or the specific characteristics of the signals being generated. The discussion also reflects varying levels of understanding about the implications of using different filtering methods.

Who May Find This Useful

This discussion may be useful for individuals interested in signal processing, particularly those working with noise generation and filtering techniques in experimental setups or applications involving vibration exciters.

veccia
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I want to generate a Gaussian white noise as an input to a vibration exciter. Since, it does not follow the high frequencies I need to low pass filter the signal. I need to implement a filter which makes minimum distortion to my signal in terms of temporal correlation of the filtered signal and at the same time attenuates the high frequencies. Can anybody help me with designing this filter? I think I should use an FIR moving average filter, but I do not know which windowing method would be useful!
 
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veccia said:
I want to generate a Gaussian white noise as an input to a vibration exciter. Since, it does not follow the high frequencies I need to low pass filter the signal. I need to implement a filter which makes minimum distortion to my signal in terms of temporal correlation of the filtered signal and at the same time attenuates the high frequencies. Can anybody help me with designing this filter? I think I should use an FIR moving average filter, but I do not know which windowing method would be useful!

Welcome to the PF.

Since the mechanical apparatus does not respond to the high frequency components of your white noise signal, why do you need to filter them out? The mechanical apparatus is acting as a lowpass filter already.
 
Agreed with Berkeman.

But assuming you do need a low-pass filter for some reason not yet clear, note that the main reason you might use a FIR or IIR rather than just doing an FFT, windowing the spectrum, and then a IFFT, is to save CPU. I'm sure any modern desktop computer has more than enough CPU horsepower to use the FFT method for the frequency range of the device!

In fact, if you are driving it with an audio output and you don't care too much about the degree of randomness, you might simply create a few minutes (or even hours) of Gaussian (white) noise with an audio editor (like Audacity), low pass it with the FFT functions provided in the editor, and play it in loop mode. Or if you don't trust that Audacity really is making AWGN, then make it yourself and use Audacity's abilty to import raw data files.

If you want more randomness than that, i wouldn't be surprised if there is free software out there that would do that dynamically.

If you are not driving it with an audio output, note that Audacity can save raw sample data files, too.
 
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The point is that I am generating two different random noises with the same mean and different sigmas in an online task and they should be updated in less than a second time resolution and moreover I need to have control over sigmas after filtering (I need to keep the sigma ratios the same as the raw signals) and almost after vibrator rather than giving the raw signal and not knowing what is the actual output of the vibrator. Vibrator is working as an unknown low-pass filter and it produces some high level auditory noise in high frequencies (probably in resonance frequencies) that I am trying to avoid it by low-pass filtering the signal before.
 
If you are generating a digital signal to drive your exciter, why not generate white noise in the frequency domain, filter the amplitude as you want, and then do an inverse FFT to convert it into the time domain?

Or if you want bandwidth-limited noise, just choose the Nyquist frequency of your digital signal generation process to kill the high frequency content that you don't want.
 
Well, then how can I have control over "sigma" of the Gaussian signal in the time domain?
 

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