Fast Fourier Transform (FFT), developed by Cooley and Tukey in 1965, is an algorithm that computes the discrete Fourier Transform (DFT) of a signal. FFT significantly reduces the complexity of the Fourier Transform algorithm by exploiting the symmetry and periodicity of a Fourier Transform and using a divide-and-conquer strategy.
The divide-and conquer-strategy that ML Engine FFT function uses is Radix-2, Radix-4, or Radix-8, for a signal whose length is a power of 2, 4, or 8, respectively.
|FFT (ML Engine)||Uses FFT algorithm to compute DFT of each signal in one or more input table columns.|
|IFFT (ML Engine)||Uses inverse Fast Fourier Transform (IFFT) algorithm (also called a Fourier synthesis algorithm) to reverse Fast Fourier Transform performed by FFT function; that is, the IFFT function takes a frequency domain representation and combines the contributions of all the different frequencies to recover the original signal.|