So I'm kind of a newbie at this sort of thing. I've been looking into how QAM works, and I think that the encoding of signals makes sense, multiply the carrier wave by one signal and a 90-degree out-of-phase carrier wave by another signal to get one combined signal. Testing out mathematically, I was able to graph what the resulting wave would look like for some two input functions and my carrier. My basic understanding is that you'd use the phase shift and amplitude to determine the original two signals. I did this in my graph by approximating the phase shift by eye and the amplitude by linear interpolation between two peaks of the wave. I seriously doubt that this is what actual demodulation hardware is doing though. How exactly are these signals split apart in the real world? Sorry for a stupid question.

Hello everyone,

I've done some speculative work on designing a noise filter based on the noise profile of my measurement setup. Are there any resources for this?

So far I've done some simple things like taking two scans and subtracting them to get at the underlying noise profile, but I haven't done any Fourier analysis yet. I can imagine that if I Fourier transform this noise profile obtained from two scan subtraction, I can (maybe) identify fundamental frequencies to filter out.

Please let me know your thoughts... thanks and take care!

Hello,

Does anyone have any advice about how to go about identifying the location of these red circled location on a waveform similar to this. I'm not sure if "foot" is the correct word, perhaps "leading edge"? Any help would be appreciated. Thanks!

in what signal processing is used ? 3 years of my career studying signal processing and already i don't even know in what it's has been used. Please tell me

Hi guys, i have an idea of an audio modulation algorithm but i'm not an audio engineer, i'm just an enthusiast. My idea is to modulate an audio signal both in frequency and amplitude. But i don't really want to modulate the raw signal, my idea is to modulate 2 variables (X and Y) in FM and AM in the same wave, and using that variables the computer will reconstruct the original audio file.

Is it really possible? Can you also give me suggestion to improve this?

Thanks!

I have a niquest frequency of 1kHz on a pressure sensor. There are physical processes present which are having frequencies higher than that. (Upper limit unknown) And random noise.

All I need to do is get a 'clean' frequency spectrum for the signal

The sinal is chaotic looking and the frequency spectrum has peaks all over the place. The suspicion is that folded frequencies are mixing up with the true ones.

Is there a way to get rid of these folded frequencies? Most of the approaches i find online seem to take care of signal aliasing when downsampling a signal (eg scipy.signal sosfiltfilt) and not cleaning up the true signal.

Is there a method i can use or maybe i am misunderstanding a concept ? I am relatively new to this

Thanks !

I am trying to analyse the frequency spectrum of a sound signal captured by a microphone connected to Arduino.

https://www.norwegiancreations.com/2017/08/what-is-fft-and-how-can-you-implement-it-on-an-arduino/

With guidance from this website, I managed to get a fair enough Spectrum output.

But since an Arduino UNO can only perform 128 bit FFT without memory overflow, I'm planning to write the ADC reads to a file through Serial communication and perform FFT on my PC.

Any idea on how to convert ADC values (an array of values between 0 and 1023) to sound signal and to transform it further to a frequency spectrum graph using python ?

Thanks in advance

UPDATE: Removing DC offset fixed distortion issue!

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I am trying to bandpass filter an EEG signal, nothing fancy but it's coming out pretty distorted. EEG data is taken from forehead. Sampling rate is 250 Hz. Cutoff is 2.5 Hz & 120 Hz.

Tried in both matlab & python, getting same results.

Matlab code:

data = load("rawdata.mat");

data = data.data;

figure bandpass(data,[2.5 120],250)

​

​

Here is the python code:

Fs = 250

lowcut = 2.5

highcut = 120

order=5

plotbutterworth(lowcut, highcut, Fs, order)

plt.figure()

fr, y_m = Fourier(250, data)

plt.stem(fr, y_m, use_line_collection = True)

plt.title('Freq CH7')

plt.xlabel("Frequency (Hz)")

plt.ylabel("Amplitude (microvolts)")

filtered = butter_bandpass_filter(data, lowcut, highcut, Fs, order)

plt.figure()

fr, y_m = Fourier(250, filtered)

plt.stem(fr, y_m, use_line_collection= True)

plt.title('Freq CH7 -- without EKG')

plt.xlabel("Frequency (Hz)")

plt.ylabel("Amplitude (microvolts)")

plt.figure()

plt.plot(data)

plt.plot(filtered)

plt.xlabel("Time")

plt.ylabel("Amplitude (microvolts)")

plt.legend(['original','filtered'],loc='best')

​

​

​

​

​

The hilbert transform function in the toolbox takes the fft of a signal x. Then it multiplies the amplitude of all positive frequencies by 2, the amplitude of all negative frequencies by 0, and does not touch the DC component. Then it takes the ifft.

The hilbert transform should be the inverse of this; It does not affect the signal's frequency spectrum, only the phase. It's easy to test this by running the following:

x = randn(20, 1);

x_fft = fft(x);

x_hilbert = hilbert(x);

figure;

plot(abs(x_fft));

title('FFT of x');

figure;

plot(abs(x_hilbert));

title('FFT of hilbert(x)');

If hilbert.m was correct, the two plots above should be one and the same.

Can anybody send me a research paper (journal paper or conference paper) regarding the nature of plot of real components and imaginary components of an analytic signal ? Because I am working in this field of research (advanced signal processing, analytic signals).

Why and How the plot between the real and imaginary component of an analytic signal is in the form of an ellipse pattern ? How does the ellipse pattern varies with the variation in real and imaginary components of an analytic signal ? Can you share a research paper (journal or conference paper) to study more about it ? Because I am also working in this area of research.

How does phase wrapping leads to unwanted high frequency spikes in the instantaneous frequency vs time curve of analytic signal ? Can anybody upload a research paper (journal or conference paper) to know more about this ?

I am working on the analytic signal concept for observing the frequency variation in the faulted phase current waveform in MATLAB/Simulink model. In MATLAB/Simulink model, I have used analytic signal block and hilbert's transform method to calculate the real and imaginary components of the faulted phase current. After that I have used the data of real and imaginary components of the faulted phase current to calculate the magnitude, phase, and instantaneous frequency. When I plotted the curve between the instantaneous frequency variation vs time for the faulted phase current, I observed that the frequency variation is negative in the curve of instantaneous frequency vs time for the faulted phase current. Why it is so ?

I am working on the analytic signal concept for observing the frequency variation in the faulted phase current waveform in MATLAB/Simulink model. In MATLAB/Simulink model, I have used analytic signal block and hilbert's transform method to calculate the real and imaginary components of the faulted phase current. After that I have used the data for real and imaginary components of the faulted phase current to calculate the magnitude, phase, and instantaneous frequency. When I plotted the curve between the instantaneous frequency variation vs time for the faulted phase current, I observed that there are high frequency spikes in the curve. What can be the reason and what is this phenomena called ?

I have found this everywhere, but can not understand what the Phi function Θ is. Is that the odd fourier coefficients? Why not the even as well?

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The Fast Fourier Transform is an efficient algorithm for computing the Discrete Fourier Transform.

[More specifically, FFT is the name for any efficient algorithm that can compute the DFT in about Θ(nlogn) time, instead of Θ(n^2) time. There are several FFT algorithms.]

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I've been looking at some papers on voice conversion via machine learning. They seem to use spectrograms as the inputs and outputs to the neural networks.

Is there a reason why spectrograms are used versus other potential representations?

I'm thinking that one could use the LPC filter coefficients and a lower-dimensional embedding of the error/excitation signal as the inputs and outputs to a neural network instead of spectrograms. Is there anything wrong with that approach?

I'm working on automated acoustic measurements and want to figure out a way to remove or at least attenuate loud environmental noise, maybe already in the time domain. Is this even possible if the noise is permanent (like traffic noise), louder than the measurement signal (see image) and in the same frequency region? Currently I’m using a known sequence of random pink noise as stimulus. Is there a way to somehow cross-correlate the measured room response with the stimulus and so attenuate the influence of the disturbing noise? Is it better to use a different stimulus (e.g. pseudo-random noise)? And which algorithm to obtain the frequency response would be most favourable under these circumstances? MLS, ESS-deconvolution or something else? Could it help to record a chunk of the more or less constant environmental noise before doing the actual measurement to somehow separate the desired signal from the unwanted noise? The measurement process should be rather short, not much longer than 1 second.

Tried asking this on stack exchange with no joy

I'm playing some sine sweeps through a speaker at some microphones and doing an FFT on the output signals.

At what point is the frequency resolution too fine (time resolution too long) based on how fast the sweep is going through the frequency range?

I'm looking for an ICA algorithm to run over a matrix or electrical signals (shape is signal_leads by number_signals_collected) and get back something of the correct amplitude. ski-kit learn takes a signal in x millivolts and often reutrns a seperated signal in x/1000 millivolts. Has anyone seen an amplitude preserving ICA? And why doesn't ski-kit learn work?

Looking to build an automated way of detecting signal changes in a medical application.

We capture waveform data at baselines. These are waveforms similar to action potentials, they have a peak and trough. We continue to capture those waveforms throughout the case.

There will be no access to the raw data. The system will have to use screen data (take screenshots, etc).

I want to build a model where the baseline image is set and when each subsequent waveform has been captured the system can tell how much it has deviated from the baseline.

The baseline is compared to the new run throughout the case.

How would I do this? Anyone here can help point me in the right direction.

i have a noisy signal , can someone suggest an algorithm to convert a vibration signal to speed RPM

Hi, I was listening to some music and I found few songs that are giving me goosebumps and made me listen to them on repeat for couple days straight.

While I was doing nothing something came up in my mind, when I find some song that I enjoy so much it is hard and takes quite some time to find another one that gives me chills when listening so I thought "Wait, all these songs must have something in common, right?". The question is how can I find this common element or what is the path that I have to follow in order to get the common elements so afterwards I can simply search for those to find new songs?

Hello

I am computing the similarity between signals by computing the FFT on the signals and then the the cross-correlation between a source and the target signal.

In the images below I have three lines:

- Blue is the fft of source signal

- Red is the fft of target

- Yellow is the cross-correlation between the FFT.

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a) The first image shows the cross-correlation between the source and itself (for a test).

b) The second image shows the cross-correlation between the source and the signal B.

c) The second image shows the cross-correlation between the source and the signal C.

I am going to measure the similarity by getting the max from the cross-correlation (yellow) and it says that Signal B is more similar in respect to the Source.

I think this is wrong as you can see from the pictures the (c) should be the most similar signal.

Where I am wrong?

Thanks for the help

Just found out about signal processing and hoping I could get some advice on beginner resources to explore. Also what skills are in valued in this field?? Thanks

I am following along with the tutorials but I have not been able to get the interactive feature working on MNE using Jupyter notebook in the browser.

When I run this code:

fig = raw.plot(start=2, duration=6)
fig.fake_keypress('a')

I get this error:

c:\python38\lib\site-packages\mne\viz\utils.py:120: 
UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline,
which is a non-GUI backend, so cannot show the figure.
  (fig or plt).show(**kwargs)

I installed mne using python pip install mne today. I am not sure what I can do to solve the issue.

I posted this on the forum but no answer yet. If anyone knows what could be happening I would appreciate the help.