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How to smooth audio FFT data - Processing 2.x and 3.x Forum
https://forum.processing.org/two/discussion/1836/how-to-smooth-audio-fft-data.html
How to smooth audio FFT data. orgicus. December 2013 edited December 2013 in Questions about Code. I was looking at this Web Audio API demo, part of this nice book. If you look at the demo, the fft peaks fall smoothly. I'm trying to do same with Processing in Java mode using the minim library.
fft - Is there a technical term for this simple method of ...
https://dsp.stackexchange.com/questions/35238/is-there-a-technical-term-for-this-simple-method-of-smoothing-out-a-signal
The problem I had was that the audio signal values changed too rapidly to produce a pleasing visual output if I just mapped the FFT values directly: So I apply a simple function to the values in order to "smooth out" the result:
A Generalized Method for Fractional-Octave …
https://www.princeton.edu/3D3A/Publications/Tylka_JAES_SmoothingWeights.pdf
the fast Fourier transform (FFT) of a discrete-time signal, wherein the spectral data points are uniformly spaced in frequency, the fractional-octave smoothing window must vary with frequency. Hatziantoniou and Mourjopoulos [5] presented a method and general framework for smoothing FFT-based frequency spectra to an arbitrary frequency res-olution.
FFT and smoothing of signal - MathWorks
https://www.mathworks.com/matlabcentral/answers/320596-fft-and-smoothing-of-signal
The fft function creates a full (two-sided) Fourier transform, dividing the energy between the negative frequencies and the positive frequencies (most easily realised by using the fftshift function and creating an appropriate frequency vector that extends from the negative Nyquist frequency to the positive Nyquist frequency). Displaying the one-sided Fourier …
Smoothing FFT graph in Python - Stack Overflow
https://stackoverflow.com/questions/44740542/smoothing-fft-graph-in-python
If you change the number of fft points to 4096, i.e. nfft=2**12, then you get a smoother graph. Remove peaks at 0 Hz If the DC value is all you care about, then just subtract the mean. Based on the example above you can change line 5 to yf = fftshift (fft (y - np.mean (y), nfft)) and you get the FFT without the baseband. Minimum number of points
fft - $1/n$ octave complex smoothing - Signal Processing ...
https://dsp.stackexchange.com/questions/16635/1-n-octave-complex-smoothing
When the size of smoothing window is defined in terms of octave, e.g., 1/3th Oct., 1/6th Oct etc., it is called fractional-octave smoothing. Let Z [ i] be the complex valued frequency spectrum, where i is the frequency index (FFT bin) and 0 ≤ i ≤ N − 1; N is the length of frequency bins. Then smoothed spectrum is given by
Intro. to Signal Processing:Smoothing
https://terpconnect.umd.edu/~toh/spectrum/Smoothing.html
Smoothing usually reduces the noise in a signal. If the noise is "white" (that is, evenly distributed over all frequencies) and its standard deviation is D, then the standard deviation of the noise remaining in the signal after one pass of a rectangular smooth will be approximately D /sqrt ( m ), where m is the smooth width.
More about FFTs - Audio Precision
https://www.ap.com/technical-library/more-about-ffts/
The bin width (also called line spacing) defines the frequency resolution of the FFT. The FFT provides amplitude and phase values for each bin. The bin width is stated in hertz. The bin width can be calculated by dividing the sample rate by the FFT length; or by dividing the bandwidth by the number of bins (which is equal to 1/2 the FFT length).
FFT - Octave Smoothing : DSP - reddit
https://www.reddit.com/r/DSP/comments/7mzik0/fft_octave_smoothing/
From my understanding, this is the power distributed among frequencies of a signal. If I have an audio signal, and I take the absolute value of the FFT of the signal, this provides be with a plot showing frequency versus amplitude. Now, if I square the absolute value, this provides me with the PSD of the signal if I am understanding correctly.
FFT - Studio Six Digital
https://studiosixdigital.com/audiotools-modules-2/acoustic-analysis-modules/fft/
Smoothing averages the FFT dB values around each graph point logarithmically. Decay Mode The decay times apply to the graph dB values. A decay time of one second will cause a point to decay at the rate of 20dB/second. Peak Hold holds the highest value received, and Average is a true linear average of all readings over the time of the average.
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