I have data from an accelerometer and made a graph of acceleration(y-axis) and time (x-axis). The frequency rate of the sensor is arround 100 samples per second. but there is no equally spaced time (for example it goes from 10.046,10.047,10.163 etc) the pace is not const. And there is no function of the signal i get. I need to find the frequency of the signal and made a graph of frequency(Hz x-axis) and acceleration (y-axis). but i don't know which code of FFT suits my case. (sorry for bad english)
Any help would be greatly appreciated
For an FFT to work you will need to reconstruct the signal you have with with a regular interval. There are two ways you can do this:
Interpolate the data you already have to make an accurate guess at where the signal would be at a regular interval. However, this FFT may contain significant inaccuracies.
OR
Adjust the device reading from the accelerometer incorporate an accurate timer such that results are always transmitted at regular intervals. This is what I would recommend.
Related
I am currently analyzing sensor signals in MATLAB.
As you see FFT is done for the raw and filtered signal.
This is just an exemplary filter, which I need to adjust.
However, I am having trouble deciding which signal frequencies are relevant and which should be filtered out.
I do know that the highest peaks in frequency spectrum depict the most important, however they are not always a clearly visible as on the screenshot which I sent you.
Do you know any methods of determining the most relevant frequencies or maybe you could recommend some articles/literature which would explain it ?
We can't tell you what the important frequencies are, that's for you to decide. Why are you doing an FFT? There must be something you're trying to evaluate. Whatever is driving you to run an FFT should give you hints about what frequencies to look at.
You could look for fundamental frequencies, but that's just a guess. The harmonics and their relative magnitudes are just as important - they determine the timbre of the sound. A saxophone, a clarinet, a trumpet, and a flute may all play the same fundamental frequency/note, but they sound different because their harmonics generate different timbres.
I am new to Matlab and signal processing. I am having an issue with defining the frequency range in which the spectrogram is processed. When I am plotting the spectrogram of .wav audio data, the y axis, frequency, spans from zero to around 23 kHz. The useful data I am looking for is in the range of 200-400 Hz. My code snippet is:
[samFa, fs] = audioread('samFa.wav'); %convert audio to numerical data
samFa = samFa(:,1); %take only one channel of numerical output
spectrogram(samFA,2205,1200,12800, fs,'yaxis','MinThreshold',-80);
I don't want to be some noobie that runs into a problem and instantly gives up and posts a duplicate question to stackoverflow, so I have done as much digging as I can, but am at my wit's end.
I scoured the documentation for parameters or ways to have Matlab only analyze a subset or range of the data, but found nothing. Additionally, in all of the examples the frequency range seems to automatically adapt to the data set.
I know it is possible to just calculate the spectrogram for the entire range of frequencies, and then remove all of the unnecessary data through truncating or manually changing the limits in the plot itself, but changing plotting limits does not help with the numerical data.
I went searching through many similar questions, and found an answer all the way from 2012 here: Can I adjust spectogram frequency axes?
where the suggested answer was to import a vector of specific frequencies for the spectrogram to analyze. I tried passing a vector of integer values between 200 and 400, and a few other test ranges, but got the error:
Error using welchparse>welch_options (line 297)
The sampling frequency must be a scalar.
I've tried passing the parameter in at different places in the function, with no avail, and don't see anything regarding this parameter in the documentation, leading me to believe that this functionality was possibly removed sometime between 2012 and now.
When plotting spectrogram without providing signal frequency, Matlab provides a normalized spectrogram, which only provides a much smaller data window, which I can visually assess to cover the data from 0:5kHz (an artifact of overtones in the audio), so I know that matlab is not finding any data above this range to make the frequency range go to 20kHz
I've been trying to learn some signal processing for this project, so I believe the Nyquist frequency should be the maximum frequency that a Fourier transform is able to analyze, to be half the sampling frequency. My recording frequency is sampling at 44,100 Hz, and the spectrogram is ranging to around 22 or 23 kHz, leading me to believe that it's Matlab is noticing my sampling frequency and assuming that it needs to analyze up to such a high range.
For my work I am doing I am needing to produce thousands of spectrograms to then be processed through much further analysis, so it is very time consuming for Matlab to be processing so much unecessary data, and I would expect there to be some functionality in Matlab somehow to get around this.
Sorry for the very long post, but I wanted to fully explain my problem and show that I have done as much work as I could to solve the problem before turning for help. Thank you very much.
Get the axis handle and set the visual range there:
spectrogram(samFA,2205,1200,12800, fs,'yaxis','MinThreshold',-80);
ax=gca;
ylim(ax, [0.2,0.4]); %kHz
And if you want to calculate specific frequencies range to save time you better use goertzel.
f = 200:10:400;
freq_indices = round(f/fs*N) + 1;
dft_data = goertzel(data,freq_indices);
I am using the Lomb-Scargle code to estimate some frequencies in a short time-series, the time series is shown in the first image. The results of the Lomb-Scargle analysis are shown in the second, and I have zoomed in on a prominent peak at about 2 cycles per day. However this peak is smeared and thus it is proving difficult to resolve the real frequency of this component. Is there any other methods, or improvements to the method I am using, to accurately resolve the important frequency components within this short time-series?
There is some information on the use of methods for short time series here but its not clear whether they need to be regularly sampled. Ideally I am looking for a method that works with irregularly sampled data, from some research it appears that maximum entropy methods are the answer, but I am not sure whether these have been implemented in MATLAB? Although from the this link, it appears that there is an equivalent method, 'The Yule-Walker AR method produces the same results as a maximum entropy estimator. However again its not clear whether the data need to be uniformly sampled?
I use Matlab to calculate the fft result of a time series data. The signal has an unknown fundamental frequency (~80 MHz in this case), together with several high order harmonics (1-20th order). However, due to finite sampling frequency (500 MHz in this case), I always get the mixing frequencies from high order frequency (7-20), e.g. 7th with a peak at abs(2*500-80*7)=440 MHz, 8th with frequency 360 MHz and 13th with a peak at abs(13*80-2*500)=40 MHz. Does anyone know how to get rid of these artificial mixing frequencies? One possible way is to increase the sampling frequency to sufficient large value. However, my data set has fixed number of data and time range. So the sampling frequency is actually determined by the property of the data set. Any solutions to this problem?
(I have image for this problem but I don't have enough reputation to post a image. Sorry for bring inconvenience for understanding this question)
You are hitting on a fundamental property of sampling - when you sample data at a fixed frequency fs, you cannot tell the difference between two signals with the same amplitude but different frequencies, where one has f1=fs/2 - d and the other has f2=f2/2 + d. This effect is frequently used to advantage - for example in mixers - but at other times, it's an inconvenience.
Unless you are looking for this mixing effect (done, for example, at the digital receiver in a modern MRI scanner), you need to apply a "brick wall filter" with a cutoff frequency of fs/2. It is not uncommon to have filters with a roll-off of 24 dB / octave or higher - in other words, they let "everything through" below the cutoff, and "stop everything" above it.
Data acquisition vendors will often supply filtering solutions with their ADC boards for exactly this reason.
Long way to say: "That's how digitization works". But it's true - that is how digitization works.
Typically, one low-pass filters the signal to below half the sample rate before sampling. Otherwise, after sampling, there is usually no way to separate any aliased high frequency noise (your high order harmonics) from the more useful spectrum below (Nyquist) half the sample rate.
If you don't filter the signal before sampling it, the defect is inherent in the sample vector, not the FFT.
I have just recorded a tiny section of audio in MatLab using the >> x = wavrecord( 2*fs, fs, ‘double’); command.
So far I have figured out how to playback the audio using soundsc(x, fs); and to plot to a graph using plot(x).
Here is a zoomed in screen capture of my wave:
Does the x-axis display the duration in milliseconds and the y-axis display the amplitude?
I think I may have switched off the axis names by accident.
I need to confirm this before i try to calculate the Fundamental Period and Fundamental Frequency of the wave.
If I've guessed my axis correct, am I right in thinking the fundamental period is the amount of time it takes for one periodic wave to repeat itself, making it (T)? So in this case approximately 0.0050 seconds?
And, the fundamental frequency is f0 = 1/T = 1/0.0050?
I have been playing around all day and reading so much. I hope i havent confused myself and just embarrassed myself on this site. Thanks.
Your x-axis is just sample number. This would go from 1 to the number of samples your sound recording contains. The sample time which is the time period between two samples is 1/fs. fs is your sampling frequency. So the time period for 50 samples would be 50/fs.
For most waveforms you cannot find Fundamental period easily by looking at it. As #duffymo mentions you need to compute FFT or PSD and then find the location of your first peak which is not DC. This would be your fundamental frequency.
This time trace looks pretty noise to me. It's not "frequency"; it's "frequencies".
If you do an FFT on this, you'll find that there are lots of frequencies involved. You may be able to reproduce the main signal with a smaller number of harmonics, but I doubt that it'll be just one.