package Maths; import java.util.ArrayList; /** * Class for linear convolution of two discrete signals using the convolution theorem. * * @author Ioannis Karavitsis * @version 1.0 */ public class ConvolutionFFT { /** * This method pads the signal with zeros until it reaches the new size. * * @param x The signal to be padded. * @param newSize The new size of the signal. */ private static void padding(ArrayList x, int newSize) { if (x.size() < newSize) { int diff = newSize - x.size(); for (int i = 0; i < diff; i++) x.add(new FFT.Complex()); } } /** * Discrete linear convolution function. It uses the convolution theorem for discrete signals * convolved: = IDFT(DFT(a)*DFT(b)). This is true for circular convolution. In order to get the * linear convolution of the two signals we first pad the two signals to have the same size equal * to the convolved signal (a.size() + b.size() - 1). Then we use the FFT algorithm for faster * calculations of the two DFTs and the final IDFT. * *

More info: https://en.wikipedia.org/wiki/Convolution_theorem * https://ccrma.stanford.edu/~jos/ReviewFourier/FFT_Convolution.html * * @param a The first signal. * @param b The other signal. * @return The convolved signal. */ public static ArrayList convolutionFFT( ArrayList a, ArrayList b) { int convolvedSize = a.size() + b.size() - 1; // The size of the convolved signal padding(a, convolvedSize); // Zero padding both signals padding(b, convolvedSize); /* Find the FFTs of both signals (Note that the size of the FFTs will be bigger than the convolvedSize because of the extra zero padding in FFT algorithm) */ FFT.fft(a, false); FFT.fft(b, false); ArrayList convolved = new ArrayList<>(); for (int i = 0; i < a.size(); i++) convolved.add(a.get(i).multiply(b.get(i))); // FFT(a)*FFT(b) FFT.fft(convolved, true); // IFFT convolved .subList(convolvedSize, convolved.size()) .clear(); // Remove the remaining zeros after the convolvedSize. These extra zeros came from // paddingPowerOfTwo() method inside the fft() method. return convolved; } }