I want to separate the signal from the noise with the help of a low pass filter.

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studentmatlaber 2021-5-6

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评论： Star Strider 2021-5-30

Hello to everyone. I had many signals in the time domain. I first converted these signals to the frequency domain with fft. I am sharing the image of the signal in the frequency domain with you. What I need to do now is to separate the noise from the signal by passing this noisy signal through a low pass filter. but I don't know how to do it as I've never done it before. I would be very happy if you could help with this. They said I can use the firpm command for the filter.

    %the code I used to convert to frequency domain
    Tt = Time{i,m};
    x = signal{i,m};
    L = length(Tt);                                                         
    Ts = mean(diff(Tt));                                                   
    Fs = 1/Ts;                                                             
    Fn = Fs/2; 
    X = fftshift(fft(x)/L);                                                  
    Fv2 = linspace(-Fn, Fn, L);

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Star Strider 2021-5-6

I prefer that it be uploaded here. It is easier for me to work with.

studentmatlaber 2021-5-7

@Star Strider Actually I have 24 .mat files, but I can throw 1 of them from here.

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采纳的回答

Star Strider 2021-5-8

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在 MATLAB Online 中打开

I could not figure out which of those signals you want to filter, so I arbitrarily chose ‘t1’ and ‘y1’.

They are not consistently sampled, so I used the resample function to correct that. This will eliminate the high-frequency noise, however I was not able to eliminate the baseline offset using a bandpass filter, since I do not know what constitutes the signal and what constitutes the baseline drift. I leave that to you to experiment with, using:

[yfilt,df] = bandpass(xr, [0.005 0.04], Fs, 'ImpulseResponse','iir');

Experiment with the lower passband frequency to get the result you want.

Try this —

LD = load('Data4.mat');
t = LD.t1;
y = LD.y1;
Tt = t;
x = y;
[xr,Ttr] = resample(x,Tt,4.5);
L = numel(Ttr);
Ts = mean(diff(Ttr));
Tsd = std(diff(Ttr));
Fs = 1/Ts;
Fn = Fs/2;
X = fftshift(fft(xr-mean(xr))/L);
Fv2 = linspace(-Fn, Fn, L);
figure;
plot(Fv2, abs(X));
grid;
xlabel('Frequency');
ylabel('Amplitude');
xlim([-0.25 0.25])
[yfilt,df] = lowpass(xr, 0.04, Fs, 'ImpulseResponse','iir');
figure
subplot(2,1,1)
plot(Ttr, xr, '-b')
grid
title('Original Signal')
subplot(2,1,2)
plot(Ttr, yfilt, '-r')
grid
title('Filtered Signal')

I cannot run this in the online application since I cannot load .mat files in it.

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studentmatlaber 2021-5-30

在 MATLAB Online 中打开

@Star Strider Hello again, sorry for asking questions all the time. There was no phase difference when I used the filtfilt command. however, this command does not work with any value other than [0.04 0.5]. When you use these values, too much noise remains in the signal. I want to narrow this range. Do you have an idea?

[n, fo, ao, w] = firpmord ([0.04 0.5], [1 0], [0.001 0.001], Fs{i,m});

Star Strider 2021-5-30

That vector defines the frequencies, and they are not allowed to be less than or equal to 0 or greater than ‘Fs/2’. Check Fs and make appropriate changes depending on that value.

That is the only possibility I can think of.

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Image Analyst 2021-5-11

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What is i and m?

What I'd do it do just zero out the middle of the FFT signal to "zero out" high frequencies. Or zero out the beginning and end of the fft if you shifted it with fftshift() so that the zero frequency point is in the middle. Then inverse transform with ifft(). It's kind of old school and primitive and you have to know what range you want to zero out over (you can experiment). It's probably not as sohpisticated as what Star Strider recommended (certain special MATLAB functions), but this "manual" way might be fine for you.