Trying to use LMS adaptive filtering to remove noise with a reference signal

In summary, the conversation discusses the use of a device called fNIRS which produces noisy signals and a noise reference. The noisy signal consists of a combination of a desired signal and a noise signal, with the scalar and phase shift changing over time. The proposed solution is using an LMS filter, but there are concerns about its effectiveness and handling of a phase shift. The conversation also mentions problems with the output signal towards the end of the file.
  • #1
softwareguy
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I have a device (fNIRS, though knowledge of fNIRS probably isn't necessary to help) which produces very noisy signals and a noise reference. The noisy signal consists of a combination of a desired signal, and a noise signal, which is a scalar and phase shift of the noise reference. However, I'm pretty sure the scalar and phase shift change slightly over time.

Similar research all suggests adaptive filtering. Colleagues in another lab have been using an LMS (Least Mean Square adaptive filtering) subroutine, but its in a Matlab toolbox which we can't afford. I found an LMS filter online. After problems getting the filter to work properly, I looked at the literature, and I believe I understand how they work. However, I have some questions.

All of the explanations of LMS filters involve solving for the filter coefficients over time which will produce a desired signal from a noisy one, if you already know the desired signal. This appears to be useful to find unknown filter coefficients, but is not useful for my purposes. The filter I found appears to somewhat work if I treat the noisy signal as the desired signal, and the noise as the input, and then take out the output signal (which is theoretically modified to be as close to the desired/noisy signal as possible). This doesn't make any sense, and I feel like it must be wrong.

In addition, none of the literature seems to account for handling a phase shift. Am I missing something?

also, the output signal goes haywire towards the end of the file. I can't understand why this would be occurring.
 
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  • #2
softwareguy said:
will produce a desired signal from a noisy one, if you already know the desired signal.
Of course! What else. If you can't define the difference between noise and signal, then you can't design a filter. I'm sure you know that, so your question is ill formed.

As for going haywire at the end, you don't give enough info for us to guess.
 

1. What is LMS adaptive filtering?

LMS (Least Mean Square) adaptive filtering is a type of algorithm used in signal processing to adjust filter coefficients based on an error signal. It is commonly used for noise cancellation and signal enhancement.

2. How does LMS adaptive filtering work?

LMS adaptive filtering works by continuously adjusting the filter coefficients based on the error signal between the desired output and the actual output of the system. This process is repeated until the error is minimized, resulting in an optimized filter that can remove noise from a signal.

3. What is a reference signal in LMS adaptive filtering?

A reference signal is a known signal that is used as a reference for the system to compare against the input signal. It is usually a clean version of the input signal, allowing the system to identify and remove any noise in the input signal.

4. What are the benefits of using LMS adaptive filtering for noise removal?

LMS adaptive filtering offers several benefits for noise removal, including its ability to adapt to different types of noise and its real-time processing capabilities. It also does not require prior knowledge of the noise characteristics, making it a versatile and efficient method for removing noise from a signal.

5. Are there any limitations to using LMS adaptive filtering for noise removal?

While LMS adaptive filtering is a powerful tool for noise removal, it does have limitations. It can struggle with highly correlated noise and may require a large number of iterations to achieve optimal results. It also relies on the quality of the reference signal, so it may not be effective for all types of noise.

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