File:Processing losses for 3 window functions.gif

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Summary

Description
English: Comparison of three window functions in terms of their effects on equal-strength sinusoids with additive noise. The noise "floor" is smoothed by averaging many DFTs to reveal the substantial difference in levels, caused by the different window functions. In each case, the sinusoid on the left suffers no scalloping and the one on the right exhibits worst-case scalloping. The rectangular window produces the most scalloping but lowest noise-floor. The Hann window has a higher noise floor but much less potential scalloping, which results in the lowest "worst case processing loss" of these 3 functions.
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Source Own work
Author Bob K
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This graphic was created with the help of the following Octave script: 

pkg load signal
graphics_toolkit gnuplot
clear all; close all; clc

  hfig= figure("position",[100 0 574 609]);
 
  N = 256;                % sams_per_fft;
  window1 = rectwin(N)'/sum(rectwin(N));
  window2 = hann(N)'/sum(hann(N));
  window3 = blackmanharris(N)'/sum(blackmanharris(N));
%
  Fs = N;                 % sams_per_sec;
  HzPerBin = Fs/N;
  F1 = -20.0*HzPerBin;    % bin -20
  F2 = +20.5*HzPerBin;    % bin 20.5
%
  L = 100000;
  n = 1:L;
  x = exp(j*2*pi*F1/Fs*n) + exp(j*2*pi*F2/Fs*n);
  x = x + (randn(1,L) +j*randn(1,L))*1.4;
%
  sams_per_offset = 0.75*N;    % overlap = 25%
%
% number of samples available beyond just one FFT
  excess_sams = length(x) - N;
%
  j1 = floor( excess_sams / sams_per_offset );
  sams_per_offset = floor( excess_sams / j1 );
  num_ffts = 1 + j1;
%
% define the first block of data
  samples = 1:N;
%
  amplitude1 = zeros(1,N);
  amplitude2 = zeros(1,N);
  amplitude3 = zeros(1,N);
%
% Loop over all the available blocks
  for j1 = 1 : num_ffts
    amplitude1 = amplitude1 + abs(fft( x(samples) .* window1 ));
    amplitude2 = amplitude2 + abs(fft( x(samples) .* window2 ));
    amplitude3 = amplitude3 + abs(fft( x(samples) .* window3 ));
    samples = samples + sams_per_offset;
  end
%
  amplitude1  = 20*log10(fftshift(amplitude1/num_ffts));
  amplitude2  = 20*log10(fftshift(amplitude2/num_ffts));
  amplitude3  = 20*log10(fftshift(amplitude3/num_ffts));
% 
  abscissa = -40:40;
  set(gca,'FontSize',8)
  subplot(3,1,1);
  h = area(abscissa, amplitude1(abscissa +N/2+1), ...
  'FaceColor', [.871 .49 0], 'edgecolor', [.871 .49 0]);
  set(h,'BaseValue',-20)
  set(gca, 'XTick', [-20 0 20.5], 'XTickLabel',[' '; ' '; ' '], 'YGrid','on', 'color', 'white')
  title('Processing losses for sinusoids in additive noise','fontsize', 12);
  xlim([-40 40])
  ylim([-20 2])
  ylabel('decibels')
  text(-40, 3.5, 'Rectangular window')
%
  subplot(3,1,2);
  h = area(abscissa, amplitude2(abscissa +N/2+1), ...
  'FaceColor', [.871 .49 0], 'edgecolor', [.871 .49 0]);
  set(h,'BaseValue',-20)
  set(gca, 'XTick', [-20 0 20.5], 'XTickLabel',[' '; ' '; ' '], 'YGrid','on', 'color', 'white')
  xlim([-40 40])
  ylim([-20 2])
  ylabel('decibels')
  text(-40, 3.5, 'Hann window')
%
  subplot(3,1,3);
  h = area(abscissa, amplitude3(abscissa +N/2+1), ...
  'FaceColor', [.871 .49 0], 'edgecolor', [.871 .49 0]);
  set(h,'BaseValue',-20)
  set(gca, 'XTick', [-20 0 20.5], 'XTickLabel',[' '; ' '; ' '], 'YGrid','on', 'color', 'white')
  xlim([-40 40])
  ylim([-20 2])
  ylabel('decibels')
  text(-40, 3.5, 'Blackman-Harris')

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25 February 2013

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Date/TimeThumbnailDimensionsUserComment
current16:37, 3 March 2013Thumbnail for version as of 16:37, 3 March 2013574 × 609 (21 KB)wikimediacommons>Bob KShorten three horizontal lines to make the scalloped peaks more apparent.

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