Contents
What is block size FFT?
The FFT works on blocks of samples which are power of 2, i.e. 256, 1024, 2048 samples coming up to 5.8ms, 23.2ms, 46.4ms at a sample rate of 44100Hz. Each “point” of the FFT represents the energy of a frequency range. One could argue that the solution to this problem is a larger block size like 65536 samples.
What is FFT averaging?
You can perform averaged measurements on an FFT channel to improve measurement accuracy or to help compensate for a low signal-to-noise ratio. RMS averaging averages the energy (or power) of the signal, so it reduces signal fluctuations, but not the noise floor.
Which is the correct way to scale the FFT?
Fs being the sampling frequency, df the step of the frequency vector. the matlab fft outputs 2 pics of amplitude A*Npoints/2 and so the correct way of scaling the spectrum is multiplying the fft by dt = 1/Fs. Dividing by Npoints highlights A but is not the correct factor to approximate the spectrum of the continuous signal.
How is the amplitude of the FFT related to its magnitude?
The FFT returns a two-sided spectrum in complex form (real and imaginary parts), which you must scale and convert to polar form to obtain magnitude and phase. The frequency axis is identical to that of the two-sided power spectrum. The amplitude of the FFT is related to the number of points in the time-domain signal.
Is the FFT the same as the power spectrum?
The FFT returns a two-sided spectrum in complex form (real and imaginary parts), which you must scale and convert to polar form to obtain magnitude and phase. The frequency axis is identical to that of the two-sided power spectrum.
What are the basic functions of the FFT?
The basic functions for FFT-based signal analysis are the FFT, the Power Spectrum, and the Cross Power Spectrum. Using these functions as building blocks, you can create additional measurement functions such as frequency response, impulse response, coherence, amplitude spectrum, and phase spectrum.