I need to know a way to make FFT (DFT) work with just n points, where n is not a power of 2.
I want to analyze an modify the sound spectrum, in particular of Wave-Files, which have in common 44100 sampling points. But my FFT does not work, it only works with points which are in shape like 2^n.
So what can I do? Beside fill up the vector with zeros to the next power of 2 ?!
Any way to modify the FFT algorithm?
Thanks!
You can use the FFTW library or the code generators of the Spiral project. They implement FFT for numbers with small prime factors, break down large prime factors p by reducing it to a FFT of size (p-1) which is even, etc.
However, just for signal analysis it is questionable why you want to analyze exactly one second of sound and not smaller units. Also, you may want to use a windowing procedure to avoid the jumps at the ends of the segment.
Aside from padding the array as you suggest, or using some other library function, you can construct a Fourier transform with arbitrary length and spacing in the frequency domain (also for non-integer sample spacings).
This is a well know result and is based on the Chirp-z transform (or Bluestein's FFT). Another good reference is given by Rabiner and can be found at the above link.
In summary, with this approach you don't have to write the FFT yourself, you can simply use an existing high-performance FFT and then apply the convolution theorem to a suitably scaled and conditioned version of your signal.
The performance will still be, O(n*log n), multiplied by some implementation-dependent scaling factor.
The FFT is just a faster method of computing the DFT for certain length vectors; and a DFT can be computed for any length of input vector. You can also zero-pad your input vector to a length supported by your FFT library, which may be faster.
If you want to modify your sound file, you may need to use the overlap-add or overlap-save fast convolution filtering after determining the length of the impulse response of your frequency domain modification.
Related
I have time series data that I want to decompose into its intrinsic frequencies. After running FFT on the series, I am left with a periodogram with the main frequencies.
Is there any way to extract a specific frequency and convert it back into the original time-domain?
For example, if I find a 7-day frequency, I can understand that this is a weekly cycle, however, is there any way to understand on what day the peaks occur?
Use a complex FFT instead of a scalar periodogram. The magnitude of a complex FFT shows the peaks, but the complex inverse (complex IFFT) of a single complex FFT peak (might be more than one sample in width) will retain the phase information and result in the proper time relationship of a particular cyclic component to the original time series.
Note: The input to the complex IFFT needs to retain conjugate symmetry for the result to be real instead of complex (except for rounding/quantization noise).
I have some geographical trajectories sampled to analyze, and I calculated the histogram of data in spatial and temporal dimension, which yielded a time domain based feature for each spatial element. I want to perform a discrete FFT to transform the time domain based feature into frequency domain based feature (which I think maybe more robust), and then do some classification or clustering algorithms.
But I'm not sure using what descriptor as frequency domain based feature, since there are amplitude spectrum, power spectrum and phase spectrum of a signal and I've read some references but still got confused about the significance. And what distance (similarity) function should be used as measurement when performing learning algorithms on frequency domain based feature vector(Euclidean distance? Cosine distance? Gaussian function? Chi-kernel or something else?)
Hope someone give me a clue or some material that I can refer to, thanks~
Edit
Thanks to #DrKoch, I chose a spatial element with the largest L-1 norm and plotted its log power spectrum in python and it did show some prominent peaks, below is my code and the figure
import numpy as np
import matplotlib.pyplot as plt
sp = np.fft.fft(signal)
freq = np.fft.fftfreq(signal.shape[-1], d = 1.) # time sloth of histogram is 1 hour
plt.plot(freq, np.log10(np.abs(sp) ** 2))
plt.show()
And I have several trivial questions to ask to make sure I totally understand your suggestion:
In your second suggestion, you said "ignore all these values."
Do you mean the horizontal line represent the threshold and all values below it should be assigned to value zero?
"you may search for the two, three largest peaks and use their location and probably widths as 'Features' for further classification."
I'm a little bit confused about the meaning of "location" and "width", does "location" refer to the log value of power spectrum (y-axis) and "width" refer to the frequency (x-axis)? If so, how to combine them together as a feature vector and compare two feature vector of "a similar frequency and a similar widths" ?
Edit
I replaced np.fft.fft with np.fft.rfft to calculate the positive part and plot both power spectrum and log power spectrum.
code:
f, axarr = plt.subplot(2, sharex = True)
axarr[0].plot(freq, np.abs(sp) ** 2)
axarr[1].plot(freq, np.log10(np.abs(sp) ** 2))
plt.show()
figure:
Please correct me if I'm wrong:
I think I should keep the last four peaks in first figure with power = np.abs(sp) ** 2 and power[power < threshold] = 0 because the log power spectrum reduces the difference among each component. And then use the log spectrum of new power as feature vector to feed classifiers.
I also see some reference suggest applying a window function (e.g. Hamming window) before doing fft to avoid spectral leakage. My raw data is sampled every 5 ~ 15 seconds and I've applied a histogram on sampling time, is that method equivalent to apply a window function or I still need apply it on the histogram data?
Generally you should extract just a small number of "Features" out of the complete FFT spectrum.
First: Use the log power spec.
Complex numbers and Phase are useless in these circumstances, because they depend on where you start/stop your data acquisiton (among many other things)
Second: you will see a "Noise Level" e.g. most values are below a certain threshold, ignore all these values.
Third: If you are lucky, e.g. your data has some harmonic content (cycles, repetitions) you will see a few prominent Peaks.
If there are clear peaks, it is even easier to detect the noise: Everything between the peaks should be considered noise.
Now you may search for the two, three largest peaks and use their location and probably widths as "Features" for further classification.
Location is the x-value of the peak i.e. the 'frequency'. It says something how "fast" your cycles are in the input data.
If your cycles don't have constant frequency during the measuring intervall (or you use a window before caclculating the FFT), the peak will be broader than one bin. So this widths of the peak says something about the 'stability' of your cycles.
Based on this: Two patterns are similar if the biggest peaks of both hava a similar frequency and a similar widths, and so on.
EDIT
Very intersiting to see a logarithmic power spectrum of one of your examples.
Now its clear that your input contains a single harmonic (periodic, oscillating) component with a frequency (repetition rate, cycle-duration) of about f0=0.04.
(This is relative frquency, proprtional to the your sampling frequency, the inverse of the time beetween individual measurment points)
Its is not a pute sine-wave, but some "interesting" waveform. Such waveforms produce peaks at 1*f0, 2*f0, 3*f0 and so on.
(So using an FFT for further analysis turns out to be very good idea)
At this point you should produce spectra of several measurements and see what makes a similar measurement and how differ different measurements. What are the "important" features to distinguish your mesurements? Thinks to look out for:
Absolute amplitude: Height of the prominent (leftmost, highest) peaks.
Pitch (Main cycle rate, speed of changes): this is position of first peak, distance between consecutive peaks.
Exact Waveform: Relative amplitude of the first few peaks.
If your most important feature is absoulute amplitude, you're better off with calculating the RMS (root mean square) level of our input signal.
If pitch is important, you're better off with calculationg the ACF (auto-correlation function) of your input signal.
Don't focus on the leftmost peaks, these come from the high frequency components in your input and tend to vary as much as the noise floor.
Windows
For a high quality analyis it is importnat to apply a window to the input data before applying the FFT. This reduces the infulens of the "jump" between the end of your input vector ant the beginning of your input vector, because the FFT considers the input as a single cycle.
There are several popular windows which mark different choices of an unavoidable trade-off: Precision of a single peak vs. level of sidelobes:
You chose a "rectangular window" (equivalent to no window at all, just start/stop your measurement). This gives excellent precission of your peaks which now have a width of just one sample. Your sidelobes (the small peaks left and right of your main peaks) are at -21dB, very tolerable given your input data. In your case this is an excellent choice.
A Hanning window is a single cosine wave. It makes your peaks slightly broader but reduces side-lobe levels.
The Hammimg-Window (cosine-wave, slightly raised above 0.0) produces even broader peaks, but supresses side-lobes by -42 dB. This is a good choice if you expect further weak (but important) components between your main peaks or generally if you have complicated signals like speech, music and so on.
Edit: Scaling
Correct scaling of a spectrum is a complicated thing, because the values of the FFT lines depend on may things like sampling rate, lenght of FFT, window, and even implementation details of the FFT algorithm (there exist several different accepted conventions).
After all, the FFT should show the underlying conservation of energy. The RMS of the input signal should be the same as the RMS (Energy) of the spectrum.
On the other hand: if used for classification it is enough to maintain relative amplitudes. As long as the paramaters mentioned above do not change, the result can be used for classification without further scaling.
I am trying to implement FFT, and I am OK with the code etc, but the general order of things is confusing me.
Am I right in thinking that this is the correct order of things to do?
Input -> Overlap input -> Windowing -> FFT -> Phase calculations/Overlap compensation -> Output
I'm getting results close to my input frequency, but they are consistently off by some factor that I can't work out, i.e. 440Hz is always 407Hz, 430Hz is always 420Hz.
The main bit that is confusing me is the initial overlap, as I have been looking at some open source FFT code and that is the part that I can never quite work out whats going on. I seem to be getting the idea from looking at those that overlapping is supposed to happen before windowing, but to me logically, wouldn't that mess with the windowing?
Any advice would be great
Thanks
The FFT is a discrete version of the continuous Fourier Transform.
The FFT produces a 1D vector of complex numbers. This complex vector is often used to calculate a 2D matrix of Frequency Magnitude versus Frequency, and represented as a 2D graph, like this one:
A single FFT is used when you want to understand the frequency spectrum of a signal. For example, from the above FFT graph we can say that most of the energy in this female soprano's G5 note is concentrated in the 784 Hz and 1572 Hz frequencies.
STFT or "Short-Time Fourier Transform" uses a sliding-frame FFT to produce a 2D matrix of Frequency versus Time, often represented as a graph called a Spectrogram, like this one:
The STFT is used when you want to know at what time a particular frequency event occurs in the signal. For example, from the above graph we can say that a large portion of the energy in this vocal phrase occurred between 0.05 and 0.15 seconds, in the frequency range of 100 Hz to 1500 Hz.
The workflow for the FFT is:
Sample the signal -> Window the entire sample frame -> FFT -> Calculate magnitude and phase -> Output something, usually a 2D graph
If your time-domain data is available in text form and if you can post it here, we can try to help you analyze it, or you can analyze it yourself with this online FFT: Sooeet FFT calculator
If you use window for FFT, your computation will be a kind of STFT.
There are some prepared codes of STFT like 'Spectrogram' etc.
To write the code by FFT, the overlapping is inevitable,but you can use some optimization methods to minimize ghost effects.Also, the practical way for windowing may be choosing the window's bandwidth according to frequency extension. It is clear that in high frequency data's you need to select small windows which is so time consuming.
I am not good enough in Matlab to write this code adhesively:)
Good Luck
Lately I have been experimenting with audio and FFTs, specifically the Minim library in Processing (basically Java, not that its particularly important for this question). What I have come to understand is that with a buffer/sample size N and sample rate K, after performing a forward FFT, I will get N frequency bins (only N/2 usable data and in fact Minim only returns N/2 bins) linearly spaced representing the spectrum from 0 to K/2 HZ.
With Minim (as well as other typical FFT implementations) you wait to gather N samples, and then perform the forward transformation, then wait for N more samples, and so on. In order to get a reasonable frame-rate (for audio visualizations, beat detection, etc.), I must use a small sample size relative to the sampling frequency.
The problem with this, though, is that a small sample size results in a very low resolution for the low end of the spectrum when I compute logarithmically spaced averages (Since a bass octave is much narrower than a high pitched octave).
I was wondering if a possible way to squeeze more apparent resolution would be to perform FFTs more often than every N samples on a slightly larger sample size than I am currently using. (I.E. with input buffer of size 2048, every 100 samples, add those samples to the input buffer and remove the oldest 100 samples, and perform a FFT). It seems like this would possibly create a rolling-average type of affect (which I can live with) but I'm not too sure.
What would be the pros and cons of this approach? Are there any other ways I could increase my apparent resolution while still being able to do real-time visualization and analysis?
That approach goes by the name Short-time Fourier transform. You get all the answers to your question on wikipedia: https://en.wikipedia.org/wiki/Short-time_Fourier_transform
It works great in practice and you can even get better resolution out of it compared to what you would expect from a rolling window by using the phase difference between the fft's.
Here is one article that does pitch shifting of audio signals. The way how to get higher frequency resolution is well explained: http://www.dspdimension.com/admin/pitch-shifting-using-the-ft/
We use the approach you describe, which we call overlapping, to make sure all the rows of a spectral waterfall are filled in. Overlap can be used to provide spectra that are spaced as closely as a single sample interval.
The primary disadvantage is the extra processing to produce all those spectra.
On the positive side, while the time resolution of each spectra is still constrained by FFT size, looking at closely spaced adjacent spectra seems to provide a kind of a visual interpolation that, I think, lets you see the data with higher precision.
One common way this is done is to use multiple lengths of windowed FFTs on the same data, short FFTs for good time resolution, much longer FFTs for better frequency resolution of lower frequencies. Then the problem for visualization becomes picking the best FFT result out of several possible at each plot point (such as the highest contrast sub-block, etc.) and blending them attractively.
Most modern processors (in PCs and mobile phones, etc.) can easily do multiple lengths (dozens) of FFTs still in real-time for audio.
Assume a sequence of numbers (wave-like data). I perform then the DFT (or FFT) transform. Next step I want to achieve is to find the frequencies, that correspond to the real frequencies that are included in data. As we know, DFT output has real and imaginary part a[i] and b[i]. If we look at spectrum (sqrt(a[i]^2+b[i]^2) then the maximum in it corresponds to the frequency that is included to the data. The question is how to find all frequencies from DFT? The problem arises when there are many other peaks that can be falsely selected.
I had a similar problem when doing spectral analysis processing of data when I was writing my honours thesis.
You are right: To find dominant frequencies you generally only need to look at the magnitude of the complex value in the DFT.
Unfortunately, you pretty much have to write some sort of intelligent algorithm which will identify the peaks (frequencies). The way the algorithm works is highly dependent on what the DFT looks like for your application. My DFTs all had similar characteristics, so it wasn't too difficult to put together a heuristic algorithm. If your DFT can take on any form, then you will probably get a lot of false positives and/or false negatives.
The way I did it was to identify regions in the DFT with high magnitude (peaks) which were surrounded by low magnitude (troughs). You can define the minimum difference between peaks and troughs (the sensitivity) as a constant times the standard deviation of the data. Additionally, you can say that any peaks that fall below a certain magnitude (threshold) are ignored altogether, as they are just noise.
Of course, the above technique will only really work if you have relatively well defined frequencies in your data. If your DFT is highly random, then you will need to take extra care to set the sensitivity and threshold carefully.
Don't forget that the magnitude of your data is symmetric, so you only need to look at half of it.
Once you have identified the frequencies in your DFT, don't forget to convert it into the units you want. From memory, if you have n samples taken with time discretisation dt, then if you have a peak at data point 5 (for example), where the first data point is 1, then the frequency is 1/(n*dt) radians per time unit. (I haven't done this in a while, so that formula might be off by a factor of Pi or something)