Window function improvemenets: ...
fix off-by-one inconsistencies in the sum-of-cosines windows. Implement derivatives of the window functions, needed for reassigned spectrograms.
This commit is contained in:
Paul Licameli
parent
ec742f76e7
commit
c4f7e25c1c
327
src/FFT.cpp
327
src/FFT.cpp
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@ -514,82 +514,323 @@ const wxChar *WindowFuncName(int whichFunction)
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}
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}
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void WindowFunc(int whichFunction, int NumSamples, float *in)
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void NewWindowFunc(int whichFunction, int NumSamples, bool extraSample, float *in)
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{
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int i;
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double A;
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if (extraSample)
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--NumSamples;
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switch( whichFunction )
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{
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switch (whichFunction) {
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default:
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fprintf(stderr,"FFT::WindowFunc - Invalid window function: %d\n",whichFunction);
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fprintf(stderr, "FFT::WindowFunc - Invalid window function: %d\n", whichFunction);
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break;
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case eWinFuncRectangular:
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// Multiply all by 1.0f -- do nothing
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break;
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case eWinFuncBartlett:
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{
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// Bartlett (triangular) window
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for (i = 0; i < NumSamples / 2; i++) {
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in[i] *= (i / (float) (NumSamples / 2));
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in[i + (NumSamples / 2)] *=
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(1.0 - (i / (float) (NumSamples / 2)));
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const int nPairs = (NumSamples - 1) / 2; // whether even or odd NumSamples, this is correct
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const float denom = NumSamples / 2.0f;
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in[0] = 0.0f;
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for (int ii = 1;
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ii <= nPairs; // Yes, <=
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++ii) {
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const float value = ii / denom;
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in[ii] *= value;
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in[NumSamples - ii] *= value;
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}
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// When NumSamples is even, in[half] should be multiplied by 1.0, so unchanged
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// When odd, the value of 1.0 is not reached
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}
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break;
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case eWinFuncHamming:
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{
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// Hamming
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for (i = 0; i < NumSamples; i++)
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in[i] *= 0.54 - 0.46 * cos(2 * M_PI * i / (NumSamples - 1));
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const double multiplier = 2 * M_PI / NumSamples;
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static const double coeff0 = 0.54, coeff1 = -0.46;
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for (int ii = 0; ii < NumSamples; ++ii)
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in[ii] *= coeff0 + coeff1 * cos(ii * multiplier);
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}
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break;
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case eWinFuncHanning:
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{
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// Hanning
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for (i = 0; i < NumSamples; i++)
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in[i] *= 0.50 - 0.50 * cos(2 * M_PI * i / (NumSamples - 1));
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const double multiplier = 2 * M_PI / NumSamples;
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static const double coeff0 = 0.5, coeff1 = -0.5;
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for (int ii = 0; ii < NumSamples; ++ii)
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in[ii] *= coeff0 + coeff1 * cos(ii * multiplier);
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}
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break;
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case eWinFuncBlackman:
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{
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// Blackman
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for (i = 0; i < NumSamples; i++) {
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in[i] *= 0.42 - 0.5 * cos (2 * M_PI * i / (NumSamples - 1)) + 0.08 * cos (4 * M_PI * i / (NumSamples - 1));
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}
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const double multiplier = 2 * M_PI / NumSamples;
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const double multiplier2 = 2 * multiplier;
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static const double coeff0 = 0.42, coeff1 = -0.5, coeff2 = 0.08;
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for (int ii = 0; ii < NumSamples; ++ii)
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in[ii] *= coeff0 + coeff1 * cos(ii * multiplier) + coeff2 * cos(ii * multiplier2);
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}
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break;
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case eWinFuncBlackmanHarris:
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{
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// Blackman-Harris
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for (i = 0; i < NumSamples; i++) {
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in[i] *= 0.35875 - 0.48829 * cos(2 * M_PI * i /(NumSamples-1)) + 0.14128 * cos(4 * M_PI * i/(NumSamples-1)) - 0.01168 * cos(6 * M_PI * i/(NumSamples-1));
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}
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const double multiplier = 2 * M_PI / NumSamples;
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const double multiplier2 = 2 * multiplier;
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const double multiplier3 = 3 * multiplier;
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static const double coeff0 = 0.35875, coeff1 = -0.48829, coeff2 = 0.14128, coeff3 = -0.01168;
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for (int ii = 0; ii < NumSamples; ++ii)
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in[ii] *= coeff0 + coeff1 * cos(ii * multiplier) + coeff2 * cos(ii * multiplier2) + coeff3 * cos(ii * multiplier3);
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}
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break;
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case eWinFuncWelch:
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{
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// Welch
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for (i = 0; i < NumSamples; i++) {
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in[i] *= 4*i/(float)NumSamples*(1-(i/(float)NumSamples));
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const float N = NumSamples;
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for (int ii = 0; ii < NumSamples; ++ii) {
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const float iOverN = ii / N;
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in[ii] *= 4 * iOverN * (1 - iOverN);
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}
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}
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break;
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case eWinFuncGaussian25:
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{
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// Gaussian (a=2.5)
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// Precalculate some values, and simplify the fmla to try and reduce overhead
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static const double A = -2 * 2.5*2.5;
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const float N = NumSamples;
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for (int ii = 0; ii < NumSamples; ++ii) {
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const float iOverN = ii / N;
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// full
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// in[ii] *= exp(-0.5*(A*((ii-NumSamples/2)/NumSamples/2))*(A*((ii-NumSamples/2)/NumSamples/2)));
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// reduced
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in[ii] *= exp(A * (0.25 + (iOverN * iOverN) - iOverN));
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}
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}
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break;
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case eWinFuncGaussian35:
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{
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// Gaussian (a=3.5)
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static const double A = -2 * 3.5*3.5;
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const float N = NumSamples;
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for (int ii = 0; ii < NumSamples; ++ii) {
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const float iOverN = ii / N;
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in[ii] *= exp(A * (0.25 + (iOverN * iOverN) - iOverN));
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}
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}
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break;
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case eWinFuncGaussian45:
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{
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// Gaussian (a=4.5)
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static const double A = -2 * 4.5*4.5;
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const float N = NumSamples;
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for (int ii = 0; ii < NumSamples; ++ii) {
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const float iOverN = ii / N;
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in[ii] *= exp(A * (0.25 + (iOverN * iOverN) - iOverN));
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}
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}
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break;
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}
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if (extraSample && whichFunction != eWinFuncRectangular) {
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double value = 0.0;
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switch (whichFunction) {
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case eWinFuncHamming:
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value = 0.08;
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break;
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case eWinFuncGaussian25:
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value = exp(-2 * 2.5 * 2.5 * 0.25);
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break;
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case eWinFuncGaussian35:
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value = exp(-2 * 3.5 * 3.5 * 0.25);
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break;
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case eWinFuncGaussian45:
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value = exp(-2 * 4.5 * 4.5 * 0.25);
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break;
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default:
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break;
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}
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in[NumSamples] *= value;
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}
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}
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// See cautions in FFT.h !
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void WindowFunc(int whichFunction, int NumSamples, float *in)
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{
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bool extraSample = false;
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switch (whichFunction)
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{
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case eWinFuncHamming:
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case eWinFuncHanning:
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case eWinFuncBlackman:
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case eWinFuncBlackmanHarris:
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extraSample = true;
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break;
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default:
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break;
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case eWinFuncBartlett:
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// PRL: Do nothing here either
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// But I want to comment that the old function did this case
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// wrong in the second half of the array, in case NumSamples was odd
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// but I think that never happened, so I am not bothering to preserve that
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break;
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}
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NewWindowFunc(whichFunction, NumSamples, extraSample, in);
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}
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void DerivativeOfWindowFunc(int whichFunction, int NumSamples, bool extraSample, float *in)
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{
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if (eWinFuncRectangular == whichFunction)
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{
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// Rectangular
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// There are deltas at the ends
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--NumSamples;
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// in[0] *= 1.0f;
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for (int ii = 1; ii < NumSamples; ++ii)
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in[ii] = 0.0f;
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in[NumSamples] *= -1.0f;
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return;
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}
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if (extraSample)
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--NumSamples;
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double A;
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switch (whichFunction) {
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case eWinFuncBartlett:
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{
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// Bartlett (triangular) window
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// There are discontinuities in the derivative at the ends, and maybe at the midpoint
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const int nPairs = (NumSamples - 1) / 2; // whether even or odd NumSamples, this is correct
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const float value = 2.0f / NumSamples;
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in[0] *=
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// Average the two limiting values of discontinuous derivative
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value / 2.0f;
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for (int ii = 1;
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ii <= nPairs; // Yes, <=
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++ii) {
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in[ii] *= value;
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in[NumSamples - ii] *= -value;
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}
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if (NumSamples % 2 == 0)
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// Average the two limiting values of discontinuous derivative
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in[NumSamples / 2] = 0.0f;
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if (extraSample)
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in[NumSamples] *=
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// Average the two limiting values of discontinuous derivative
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-value / 2.0f;
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else
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// Halve the multiplier previously applied
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// Average the two limiting values of discontinuous derivative
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in[NumSamples - 1] *= 0.5f;
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}
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break;
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case eWinFuncHamming:
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{
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// Hamming
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// There are deltas at the ends
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const double multiplier = 2 * M_PI / NumSamples;
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static const double coeff0 = 0.54, coeff1 = -0.46 * multiplier;
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in[0] *= coeff0;
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if (!extraSample)
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--NumSamples;
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for (int ii = 0; ii < NumSamples; ++ii)
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in[ii] *= - coeff1 * sin(ii * multiplier);
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if (extraSample)
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in[NumSamples] *= - coeff0;
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else
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// slightly different
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in[NumSamples] *= - coeff0 - coeff1 * sin(NumSamples * multiplier);
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}
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break;
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case eWinFuncHanning:
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{
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// Hanning
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const double multiplier = 2 * M_PI / NumSamples;
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const double coeff1 = -0.5 * multiplier;
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for (int ii = 0; ii < NumSamples; ++ii)
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in[ii] *= - coeff1 * sin(ii * multiplier);
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if (extraSample)
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in[NumSamples] = 0.0f;
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}
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break;
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case eWinFuncBlackman:
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{
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// Blackman
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const double multiplier = 2 * M_PI / NumSamples;
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const double multiplier2 = 2 * multiplier;
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const double coeff1 = -0.5 * multiplier, coeff2 = 0.08 * multiplier2;
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for (int ii = 0; ii < NumSamples; ++ii)
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in[ii] *= - coeff1 * sin(ii * multiplier) - coeff2 * sin(ii * multiplier2);
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if (extraSample)
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in[NumSamples] = 0.0f;
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}
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break;
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case eWinFuncBlackmanHarris:
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{
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// Blackman-Harris
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const double multiplier = 2 * M_PI / NumSamples;
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const double multiplier2 = 2 * multiplier;
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const double multiplier3 = 3 * multiplier;
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const double coeff1 = -0.48829 * multiplier,
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coeff2 = 0.14128 * multiplier2, coeff3 = -0.01168 * multiplier3;
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for (int ii = 0; ii < NumSamples; ++ii)
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in[ii] *= - coeff1 * sin(ii * multiplier) - coeff2 * sin(ii * multiplier2) - coeff3 * sin(ii * multiplier3);
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if (extraSample)
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in[NumSamples] = 0.0f;
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}
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break;
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case eWinFuncWelch:
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{
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// Welch
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const float N = NumSamples;
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const float NN = NumSamples * NumSamples;
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for (int ii = 0; ii < NumSamples; ++ii) {
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in[ii] *= 4 * (N - ii - ii) / NN;
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}
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if (extraSample)
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in[NumSamples] = 0.0f;
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// Average the two limiting values of discontinuous derivative
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in[0] /= 2.0f;
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in[NumSamples - 1] /= 2.0f;
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}
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break;
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case eWinFuncGaussian25:
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// Gaussian (a=2.5)
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// Precalculate some values, and simplify the fmla to try and reduce overhead
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A=-2*2.5*2.5;
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for (i = 0; i < NumSamples; i++) {
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// full
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// in[i] *= exp(-0.5*(A*((i-NumSamples/2)/NumSamples/2))*(A*((i-NumSamples/2)/NumSamples/2)));
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// reduced
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in[i] *= exp(A*(0.25 + ((i/(float)NumSamples)*(i/(float)NumSamples)) - (i/(float)NumSamples)));
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}
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break;
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A = -2 * 2.5*2.5;
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goto Gaussian;
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case eWinFuncGaussian35:
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// Gaussian (a=3.5)
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A=-2*3.5*3.5;
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for (i = 0; i < NumSamples; i++) {
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// reduced
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in[i] *= exp(A*(0.25 + ((i/(float)NumSamples)*(i/(float)NumSamples)) - (i/(float)NumSamples)));
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}
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break;
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A = -2 * 3.5*3.5;
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goto Gaussian;
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case eWinFuncGaussian45:
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// Gaussian (a=4.5)
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A=-2*4.5*4.5;
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for (i = 0; i < NumSamples; i++) {
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// reduced
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in[i] *= exp(A*(0.25 + ((i/(float)NumSamples)*(i/(float)NumSamples)) - (i/(float)NumSamples)));
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A = -2 * 4.5*4.5;
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goto Gaussian;
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Gaussian:
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{
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// Gaussian (a=2.5)
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// There are deltas at the ends
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const float invN = 1.0f / NumSamples;
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const float invNN = invN * invN;
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// Simplify formula from the loop for ii == 0, add term for the delta
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in[0] *= exp(A * 0.25) * (1 - invN);
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if (!extraSample)
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--NumSamples;
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for (int ii = 1; ii < NumSamples; ++ii) {
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const float iOverN = ii * invN;
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in[ii] *= exp(A * (0.25 + (iOverN * iOverN) - iOverN)) * (2 * ii * invNN - invN);
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}
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if (extraSample)
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in[NumSamples] *= exp(A * 0.25) * (invN - 1);
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else {
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// Slightly different
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const float iOverN = NumSamples * invN;
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in[NumSamples] *= exp(A * (0.25 + (iOverN * iOverN) - iOverN)) * (2 * NumSamples * invNN - invN - 1);
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}
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}
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break;
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default:
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fprintf(stderr, "FFT::DerivativeOfWindowFunc - Invalid window function: %d\n", whichFunction);
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}
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}
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38
src/FFT.h
38
src/FFT.h
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@ -45,6 +45,9 @@
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* 9: Gaussian(a=4.5)
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*/
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#include <wx/defs.h>
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#include <wx/wxchar.h>
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#ifndef M_PI
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#define M_PI 3.14159265358979323846 /* pi */
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#endif
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@ -93,18 +96,12 @@ void FFT(int NumSamples,
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float *RealIn, float *ImagIn, float *RealOut, float *ImagOut);
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/*
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* Applies a windowing function to the data in place
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*
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* 0: Rectangular (no window)
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* 1: Bartlett (triangular)
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* 2: Hamming
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* 3: Hanning
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* 4: Blackman
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* 5: Blackman-Harris
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* 6: Welch
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* 7: Gaussian(a=2.5)
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* 8: Gaussian(a=3.5)
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* 9: Gaussian(a=4.5)
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* Multiply values in data by values of the chosen function
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* DO NOT REUSE! Prefer NewWindowFunc instead
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* This version was inconsistent whether the window functions were
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* symmetrical about NumSamples / 2, or about (NumSamples - 1) / 2
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* It remains for compatibility until we decide to upgrade all the old uses
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* All functions have 0 in data[0] except Rectangular, Hamming and Gaussians
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*/
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enum eWindowFunctions
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@ -124,6 +121,23 @@ enum eWindowFunctions
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void WindowFunc(int whichFunction, int NumSamples, float *data);
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/*
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* Multiply values in data by values of the chosen function
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* All functions are symmetrical about NumSamples / 2 if extraSample is false,
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* otherwise about (NumSamples - 1) / 2
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* All functions have 0 in data[0] except Rectangular, Hamming and Gaussians
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*/
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void NewWindowFunc(int whichFunction, int NumSamples, bool extraSample, float *data);
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/*
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* Multiply values in data by derivative of the chosen function, assuming
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* sampling interval is unit
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* All functions are symmetrical about NumSamples / 2 if extraSample is false,
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* otherwise about (NumSamples - 1) / 2
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* All functions have 0 in data[0] except Rectangular, Hamming and Gaussians
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*/
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void DerivativeOfWindowFunc(int whichFunction, int NumSamples, bool extraSample, float *data);
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/*
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* Returns the name of the windowing function (for UI display)
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*/
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