I have to notch filter the following signal.
As you can see it is a signal with really high amplitude.And this causes me a lot of problems. I've designed a bandstop filter (46-55 Hz).The MATLAB code is
Fs=5000; sampling frequency
Wp_NOTCH = [36/(Fs/2) 71/(Fs/2)];
Ws_NOTCH= [46/(Fs/2) 55/(Fs/2)];
Rp_db=-20*log10(.95);
Rs_db=-20*log10(.05);
[order_NOTCH,wn_NOTCH] = buttord(Wp_NOTCH,Ws_NOTCH,Rp_db,Rs_db);
[B_NOTCH,A_NOTCH]=butter(order_NOTCH,wn_NOTCH,'stop');
And the filter is
When i filter the signal (using filtfilt) I've the following signal
As you can see it has a lot of artifacts,and "waves". Is there a way to minimize these artifacts?How can I delete the 50Hz component minimizing these artifacts?Any suggestion?
Thanks in advance
Notch filters are designed to remove sine-waves with a set frequency, they cannot work for a sharp change in the data. The example you posted does not look like a 50Hz artifact, so notch filters cannot help here. You can look-up information on how to deal with data discontinuities, jump or step artifacts, or transient response. Here is some explanation in matlab environment
Related
I am plotting some data I collected at 10000 Hz, I attached a snip of some of the data in the form of an FFT and time. I am getting a repeating frequency around 10Hz that seems to be pretty obviously some sort of noise from the system i am testing. The signal shows up in the time domain and also the frequency domain.
I am looking to use MATLAB to remove these spikes.
Has anyone dealt with a similar issue and can provide any advice.
To filter out specific frequency components of a signal, you would normally use either a notch filter or a comb filter, for which MATLAB already has some commands in the DSP System Toolbox:
https://www.mathworks.com/help/dsp/ref/iirnotch.html
https://www.mathworks.com/help/dsp/ref/fdesign.comb.html
Alternatively, if you have the Signal Processing Toolbox, you can use the band-stop Butterworth filter to remove individual frequency components (ranges) using
https://www.mathworks.com/help/signal/ref/butter.html
Could anybody explian how to use Wiener filter to revocer the audio signal from low-pass filter in Matlab?
I filtered the signal, after that I used simple deconvolution by dividing filtered signal with frequency response of the LPF, but it didnt give me any result. I didnt get restoration at all. I have checked the LPF, it works correct. I need some information about using Wiener filter for audio files, but there are only answers about deblurring images. Thank you in Advance!
I will attempt to provide some help.
You should study the normal equations as per section 11.11
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-011-introduction-to-communication-control-and-signal-processing-spring-2010/readings/MIT6_011S10_chap11.pdf
This set of equations will appear in every literature on the Wiener filter. The objective regardless of the application is to compute from a noisy observation signal a clean - but reconstructed - version of the clean input signal. Since this is a mathematical model, the quality of the reconstruction is based on how good are the computed statistics and characteristics of the "noise". Noise in audio is always uncorrelated statistically to the clean signal. This is the principle upon which the Wiener filter works.
Figure 11.3 gives an example of deconvolution in block diagram form that should suffice to solve your application in audio. A real Wiener filter minimizes the MMSE ratio as defined in 11.37. That is the difference in expectation of the square of the e(t) process.
I am new to matlab and signal processing methods, but i am trying to use its filter properties over a set of data I have. I have a collection of amplitude values obtained at different timestamps. When this is plotted, I get a waveform with several peaks that I can identify. I then perform calculations to derive the time between each consecutive peak and I want to eliminate the rates that are around the range of 48-52peaks per second.
What would be the correct way to go about processing this data step by step? Would a bandstop or notch filter be better if I want to eliminate those frequencies and not attenuate it simply? I am completely lost in the parameters required to feed into the filters for this. Please help...
periodogram is OK, but I would suggest using pwelch instead. It makes a more reasonable PSD estimate and the default parameters are well thought out (Hann windows, 50% overlap of segments, etc.)
If what you want is to remove signals in a wide band (e.g. 48-52 Hz) equally, rather than a single and unchanging frequency, than a bandstop filter is ideal. For example:
fs = 2048;
y = rand(fs*8, 1);
[b,a] = ellip(4, 2, 40, [46 54]/(fs/2));
yy = filter(b,a,y);
This will use a 4th order elliptic bandstop filter to filter the random data variable 'y'. filtfilt.m is also a nice function; it applies the filter forwards and backwards so you get twice the filter action and none of the phase lag or dispersion.
I am currently doing something similar to what you are doing.
I am processing a lot of signals from the Inertial Measurement Unit and motor drives. They all are asynchronously obtained, i.e. they all have very different timestamp and also very different acquisition frequency.
First thing I did was to interpolate all signals data in order to have all signals with same timestamp. You can use the matlab function interp to do this.
After this, you will have all signals with same sample frequency and also timestamp, which will be good in further analysis.
Ok, another thing you can do to analyse the frequency of the peaks is to perform the fourier transform. For beginners i advice the use of periodogram function and not the fft function.
Imagine you signal is x and your sample frequency (after interpolation) is Fs.
You can now use the function periodogram available in matlab like this:
[P,f] = periodogram(x,[],[length(t)],Fs);
This will give the power vs frequency of your signal. After that you will be able to plot and take a look at the frequencies of your signal. In other words, you be able to see the frequencies of the signals that make your acquired signal.
Plot the data this way:
plot(f,P); or semilogy(f,P);
The second is the same thing as the first, but with a logarithmic scale.
After this analysis you can use the Filter Desing and Analysis Tool to design you filter. Just type fdatool in matlab and it will open the design window. Choose the filter type, the cut and pass frequencies and click in design. This tool is very intuitive.
After designing you can export the filter to workspace.
Finally you can use the filter you designed in your signal to see if its what you wanted.
Use the functions filter os filtfilt for this.
Search in the web of the matlab help for the functions I wrote to get more details.
There are a lot of examples availables too.
I hope I could help you.
Good luck.
So basically, my problem is that I have a speech signal in .wav format that is corrupted by a harmonic noise source at some frequency. My goal is to identify the frequency at which this noise occurs, and use a notch filter to remove said noise. So far, I have read the speech signal into matlab using:
[data, Fs] = wavread('signal.wav');
My question is how can I identify the frequency at which the harmonic noise is occurring, and once I've done that, how can I go about implementing a notch filter at that frequency?
NOTE: I do not have access to the iirnotch() command or fdesign.notch() due to the version of MATLAB I am currently using (2010).
The general procedure would be to analyse the spectrum, to identify the frequency in question, then design a filter around that frequency. For most real applications it's all a bit woolly: the frequencies move around and there's no easy way to distinguish noise from signal, so you have to use clever techniques and a bit of guesswork. However if you know you have a monotonic corruption then, yes, an FFT and a notch filter will probably do the trick.
You can analyse the signal with fft and design a filter with, among others, fir1, which I believe is part of the signal processing toolbox. If you don't have the signal processing toolbox you can do it 'by hand', as in transform to the frequency domain, remove the frequency(ies) you don't want (by zeroing the relevant elements of the frequency vector) and transform back to time domain. There's a tutorial on exactly that here.
The fft and fir1 functions are well documented: search the Mathworks site to get code examples to get you up and running.
To add to/ammend xenoclast's answer, filtering in the frequency domain may or may not work for you. There are many thorny issues with filtering in the frequency domain, some of which are covered here: http://blog.bjornroche.com/2012/08/why-eq-is-done-in-time-domain.html
One additional issue is that if you try to process your entire file at once, the "width" or Q of the filters will depend on the length of your file. This might work out for you, or it might not. If you have many files of different lengths, don't expect similar results this way.
To design your own IIR notch filter, you could use the RBJ audio filter cookbook. If you need help, I wrote up a tutorial here:
http://blog.bjornroche.com/2012/08/basic-audio-eqs.html
My tutorial uses bell/peaking filter, but it's easy to follow that and then replace it with a notch filter from RBJ.
One final note: assuming this is actually an audio signal in your .wav file, you can also use your ears to find and fix the problem frequencies:
Open the file in an audio editing program that lets you adjust filter settings in real-time (I am not sure if audacity lets you do this, but probably).
Use a "boost" or "parametric" filter set to a high gain and sweep the frequency setting until you hear the noise accentuated the most.
replace the boost filter with a notch filter of the same frequency. You may need to tweak the width to trade off noise elimination vs. signal preservation.
repete as needed (due to the many harmonics).
save the resulting file.
Of course, some audio editing apps have built-in harmonic noise reduction features that work especially well for 50/60 Hz noise.
I have a signal in matlab and what to calculate the instantaneous phase for a specific band. I want to filter the signal into this range (using a bandpass filter) and then get the instantaneous phase. I know that there are problems using some filters with non-linear phase responses, is there any way to get around this? I have found some information online about back filtering the signal but it's still a little unclear. I'd like to avoid using wavelets (they're probably overkill here). Thanks.
Unless you resort to noncasual techniques (like the filtfilt suggested in the comment by nibot), you will always have some phase distortion. Linear phase FIRs with a delay D will add a phase of 2*pi*f*D, while nonlinear phase IIRs will add phase that is not linearly dependent on f.
In both cases, it is easy to compute the phase distortion (for example, use freqz(num, den) for IIRs) and account for that distortion when interpreting the resulting measurement. Of course, you'll have meaningless results when the phase changes significantly over the frequency range you are interested in - but that's a different issue.