Filter White Noise: Designing FIR Moving Average Filter

In summary, the individual is seeking assistance with designing a filter to minimize distortion and attenuate high frequencies in a Gaussian white noise signal used as input to a vibration exciter. They are considering using an FIR moving average filter, but are unsure which windowing method would be most useful. They also mention the use of an audio editor or software to generate the noise in the frequency domain and filter it before converting it back to the time domain. However, they are concerned about maintaining control over the sigma of the signal in the time domain.
  • #1
veccia
3
0
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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  • #2
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.
 
  • #3
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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  • #4
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.
 
  • #5
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.
 
  • #6
Well, then how can I have control over "sigma" of the Gaussian signal in the time domain?
 

1. What is a FIR moving average filter?

A FIR moving average filter is a type of digital filter used in signal processing to reduce noise and smooth out a signal. It works by taking the average of a specified number of samples from the input signal and using this average as the output. This process helps to filter out high-frequency noise and keep the lower-frequency signals intact.

2. How does a FIR moving average filter work?

A FIR moving average filter works by taking a specified number of samples from the input signal and averaging them together. This average value is then used as the output for that particular sample. The process is repeated for each sample in the input signal, resulting in a smoother output signal with reduced noise.

3. What are the advantages of using a FIR moving average filter?

There are several advantages of using a FIR moving average filter, including its simplicity, effectiveness in reducing noise, and its ability to preserve the lower-frequency components of a signal. It also has a linear phase response, which means it does not introduce any phase distortion to the output signal.

4. How do you design a FIR moving average filter?

To design a FIR moving average filter, you need to determine the length of the filter, which is the number of samples used in the averaging process. This is typically based on the desired cutoff frequency and the sampling rate of the input signal. Once the filter length is determined, the filter coefficients can be calculated using a mathematical formula or a software tool.

5. What are some applications of FIR moving average filters?

FIR moving average filters have a wide range of applications in signal processing, including audio and video processing, biomedical signal analysis, and speech recognition. They are also commonly used in digital communication systems to reduce noise and improve the quality of the received signal.

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