i just started a course "Introduction to signal processing with Matlab" and i came across with this task but i don't understand how is this possible. The task ask us to create a high frequency noise signal using an FIR-filter. But the FIR-filters are made to "clear" the noise from a signal. How can you do that?
for example a white-noise include all frequecies, so you can filter and clear low frq. noise with a high pass filter then you have a noise with only hagh freq.
Related
I have a signal that has been distorted by narrowband noise.
A zoomed in image of it's spectral analysis:
I know that frequencies intervals, that I coloured in yellow are noise and the ones I coloured in green are the original signal.
I could design filters just by hardcoding interval values from what I see, but I would like to automate the process and make an algorithm finding the noise intervals automatically.
Could you please suggest an approach (or maybe a function) that would be suitable here?
Try using pwelch(x) a command of matlabs dsp (digital signal processing) toolbox.
This function is specialized to differ between noise and signal.
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 have a signal that is both noisy and drifts. I want to calculate the noise of the signal, but I think for this drift should not be taken into account as "noise". using the snr() funciontion in matlab will give me a really high noise value, I think because it takes into account the drift, right?
How can I calculate it? is there any function available for this?
In this picture, for instance, the noise should be around 2% right? ((22.45-22.36)/2)/22.38. (although what I really want is the SNR value)
Thank you!
Filtered signal with low pass filter with a really low frequency:
I would approach this by identifying the drift of the signal with a low pass filter. Just subtract the filtered signal from the original signal. This will lead to noise signal with low drift.
Filtering the signal might be the most difficult task, but by playing around with the filter parameters this will work
I am simulating a feature detector in noisy environment which consist of AWGN Noise and Impulsive Noise. But I am getting a strange result as Signal with Impulsive+AWGN noise has better detection probability then with Signal with only AWGN. I know its wrong at some point. How Is it possible that signal with more noise like Impulsive noise has better detection possibility. Please share your experience.
Yes, this is possible in case of stochastic resonance (http://en.wikipedia.org/wiki/Stochastic_resonance)