create experimental songmanagement branch

  - nicer abstraction of songs
  - split songloading from handling songs
  - cleanup singscreen

git-svn-id: svn://svn.code.sf.net/p/ultrastardx/svn/branches/experimental@2706 b956fd51-792f-4845-bead-9b4dfca2ff2c

1 files changed, 602 insertions, 0 deletions

diff --git a/songmanagement/src/lib/fft/UFFT.pas b/songmanagement/src/lib/fft/UFFT.pas
new file mode 100644
index 00000000..5a056a8c
--- /dev/null
+++ b/songmanagement/src/lib/fft/UFFT.pas
@@ -0,0 +1,602 @@
+{**********************************************************************
+
+  FFT.cpp
+
+  Dominic Mazzoni
+
+  September 2000
+
+***********************************************************************
+
+Fast Fourier Transform routines.
+
+  This file contains a few FFT routines, including a real-FFT
+  routine that is almost twice as fast as a normal complex FFT,
+  and a power spectrum routine when you know you don't care
+  about phase information.
+
+  Some of this code was based on a free implementation of an FFT
+  by Don Cross, available on the web at:
+
+    http://www.intersrv.com/~dcross/fft.html
+
+  The basic algorithm for his code was based on Numerican Recipes
+  in Fortran.  I optimized his code further by reducing array
+  accesses, caching the bit reversal table, and eliminating
+  float-to-double conversions, and I added the routines to
+  calculate a real FFT and a real power spectrum.
+
+***********************************************************************
+
+  Salvo Ventura - November 2006
+  Added more window functions:
+    * 4: Blackman
+    * 5: Blackman-Harris
+    * 6: Welch
+    * 7: Gaussian(a=2.5)
+    * 8: Gaussian(a=3.5)
+    * 9: Gaussian(a=4.5)
+
+***********************************************************************
+
+  This file is part of Audacity 1.3.4 beta (http://audacity.sourceforge.net/)
+  Ported to Pascal by the UltraStar Deluxe Team
+}
+
+unit UFFT;
+
+{$IFDEF FPC}
+  {$MODE Delphi}
+  {$H+} // Use long strings
+{$ENDIF}
+
+interface
+type
+  TSingleArray = array[0 .. (MaxInt div SizeOf(Single))-1] of Single;
+  PSingleArray = ^TSingleArray;
+
+  TFFTWindowFunc = (
+    fwfRectangular,
+    fwfBartlett,
+    fwfHamming,
+    fwfHanning,
+    fwfBlackman,
+    fwfBlackman_Harris,
+    fwfWelch,
+    fwfGaussian2_5,
+    fwfGaussian3_5,
+    fwfGaussian4_5
+  );
+  
+const
+  FFTWindowName: array[TFFTWindowFunc] of string = (
+     'Rectangular',
+     'Bartlett',
+     'Hamming',
+     'Hanning',
+     'Blackman',
+     'Blackman-Harris',
+     'Welch',
+     'Gaussian(a=2.5)',
+     'Gaussian(a=3.5)',
+     'Gaussian(a=4.5)'
+  );
+
+(*
+ * This is the function you will use the most often.
+ * Given an array of floats, this will compute the power
+ * spectrum by doing a Real FFT and then computing the
+ * sum of the squares of the real and imaginary parts.
+ * Note that the output array is half the length of the
+ * input array, and that NumSamples must be a power of two.
+ *)
+procedure PowerSpectrum(NumSamples: Integer; In_, Out_: PSingleArray);
+
+(*
+ * Computes an FFT when the input data is real but you still
+ * want complex data as output.  The output arrays are half
+ * the length of the input, and NumSamples must be a power of
+ * two.
+ *)
+procedure RealFFT(NumSamples: integer;
+                  RealIn, RealOut, ImagOut: PSingleArray);
+
+(*
+ * Computes a FFT of complex input and returns complex output.
+ * Currently this is the only function here that supports the
+ * inverse transform as well.
+ *)
+procedure FFT(NumSamples: Integer;
+              InverseTransform: boolean;
+              RealIn, ImagIn, RealOut, ImagOut: PSingleArray);
+
+(*
+ * Applies a windowing function to the data in place
+ *
+ * 0: Rectangular (no window)
+ * 1: Bartlett    (triangular)
+ * 2: Hamming
+ * 3: Hanning
+ * 4: Blackman
+ * 5: Blackman-Harris
+ * 6: Welch
+ * 7: Gaussian(a=2.5)
+ * 8: Gaussian(a=3.5)
+ * 9: Gaussian(a=4.5)
+ *)
+procedure WindowFunc(whichFunction: TFFTWindowFunc; NumSamples: Integer; in_: PSingleArray);
+
+(*
+ * Returns the name of the windowing function (for UI display)
+ *)
+function WindowFuncName(whichFunction: TFFTWindowFunc): string;
+
+(*
+ * Returns the number of windowing functions supported
+ *)
+function NumWindowFuncs(): integer;
+
+
+implementation
+
+uses
+  SysUtils;
+
+type TIntArray = array[0 .. (MaxInt div SizeOf(Integer))-1] of Integer;
+type PIntArray = ^TIntArray;
+type TIntIntArray = array[0 .. (MaxInt div SizeOf(PIntArray))-1] of PIntArray;
+type PIntIntArray = ^TIntIntArray;
+var gFFTBitTable: PIntIntArray;
+const MaxFastBits: Integer = 16;
+
+function IsPowerOfTwo(x: Integer): Boolean;
+begin
+   if (x < 2) then
+      result := false
+   else if ((x and (x - 1)) <> 0) then  { Thanks to 'byang' for this cute trick! }
+      result := false
+   else
+    result := true;
+end;
+
+function NumberOfBitsNeeded(PowerOfTwo: Integer): Integer;
+var i: Integer;
+begin
+  if (PowerOfTwo < 2) then begin
+    Writeln(ErrOutput, Format('Error: FFT called with size %d\n', [PowerOfTwo]));
+    Abort;
+  end;
+
+  i := 0;
+  while(true) do begin
+    if (PowerOfTwo and (1 shl i) <> 0) then begin
+      result := i;
+      Exit;
+    end;
+    Inc(i);
+  end;
+end;
+
+function ReverseBits(index, NumBits: Integer): Integer;
+var
+  i, rev: Integer;
+begin
+  rev := 0;
+  for i := 0 to NumBits-1 do begin
+    rev := (rev shl 1) or (index and 1);
+    index := index shr 1;
+  end;
+
+  result := rev;
+end;
+
+procedure InitFFT();
+var
+  len: Integer;
+  b, i: Integer;
+begin
+  GetMem(gFFTBitTable, MaxFastBits * sizeof(PSingle));
+
+  len := 2;
+  for b := 1 to MaxFastBits do begin
+    GetMem(gFFTBitTable[b - 1], len * sizeof(Single));
+    for i := 0 to len-1 do
+      gFFTBitTable[b - 1][i] := ReverseBits(i, b);
+      len := len shl 1;
+   end;
+end;
+
+function FastReverseBits(i, NumBits: Integer): Integer; {$IFDEF HasInline}inline;{$ENDIF}
+begin
+  if (NumBits <= MaxFastBits) then
+    result := gFFTBitTable[NumBits - 1][i]
+  else
+    result := ReverseBits(i, NumBits);
+end;
+
+{*
+ * Complex Fast Fourier Transform
+ *}
+procedure FFT(NumSamples: Integer;
+         InverseTransform: boolean;
+         RealIn, ImagIn, RealOut, ImagOut: PSingleArray);
+var
+  NumBits: Integer;                 { Number of bits needed to store indices }
+  i, j, k, n: Integer;
+  BlockSize, BlockEnd: Integer;
+  delta_angle: Double;
+  angle_numerator: Double;
+  tr, ti: Double;                   { temp real, temp imaginary }
+  sm2, sm1, cm2, cm1: Double;
+  w: Double;
+  ar0, ar1, ar2, ai0, ai1, ai2: Double;
+  denom: Single;
+begin
+
+   angle_numerator := 2.0 * Pi;
+
+   if (not IsPowerOfTwo(NumSamples)) then begin
+      Writeln(ErrOutput, Format('%d is not a power of two', [NumSamples]));
+      Abort;
+   end;
+
+   if (gFFTBitTable = nil) then
+      InitFFT();
+
+   if (InverseTransform) then
+      angle_numerator := -angle_numerator;
+
+   NumBits := NumberOfBitsNeeded(NumSamples);
+
+   {
+    **   Do simultaneous data copy and bit-reversal ordering into outputs...
+   }
+
+   for i := 0 to NumSamples-1 do begin
+      j := FastReverseBits(i, NumBits);
+      RealOut[j] := RealIn[i];
+      if(ImagIn = nil) then
+        ImagOut[j] := 0.0
+      else
+        ImagOut[j] := ImagIn[i];
+   end;
+
+   {
+    **   Do the FFT itself...
+   }
+
+   BlockEnd := 1;
+   BlockSize := 2;
+   while(BlockSize <= NumSamples) do
+   begin
+
+      delta_angle := angle_numerator / BlockSize;
+
+      sm2 := sin(-2 * delta_angle);
+      sm1 := sin(-delta_angle);
+      cm2 := cos(-2 * delta_angle);
+      cm1 := cos(-delta_angle);
+      w := 2 * cm1;
+
+      i := 0;
+      while(i < NumSamples) do
+      begin
+         ar2 := cm2;
+         ar1 := cm1;
+
+         ai2 := sm2;
+         ai1 := sm1;
+
+         j := i;
+         for n := 0 to BlockEnd-1 do
+         begin
+            ar0 := w * ar1 - ar2;
+            ar2 := ar1;
+            ar1 := ar0;
+
+            ai0 := w * ai1 - ai2;
+            ai2 := ai1;
+            ai1 := ai0;
+
+            k := j + BlockEnd;
+            tr := ar0 * RealOut[k] - ai0 * ImagOut[k];
+            ti := ar0 * ImagOut[k] + ai0 * RealOut[k];
+
+            RealOut[k] := RealOut[j] - tr;
+            ImagOut[k] := ImagOut[j] - ti;
+
+            RealOut[j] := RealOut[j] + tr;
+            ImagOut[j] := ImagOut[j] + ti;
+
+            Inc(j);
+         end;
+
+         Inc(i, BlockSize);
+      end;
+
+      BlockEnd := BlockSize;
+      BlockSize := BlockSize shl 1;
+   end;
+
+   {
+      **   Need to normalize if inverse transform...
+   }
+
+   if (InverseTransform) then begin
+      denom := NumSamples;
+
+      for i := 0 to NumSamples-1 do begin
+         RealOut[i] := RealOut[i] / denom;
+         ImagOut[i] := ImagOut[i] / denom;
+      end;
+   end;
+end;
+
+(*
+ * Real Fast Fourier Transform
+ *
+ * This function was based on the code in Numerical Recipes in C.
+ * In Num. Rec., the inner loop is based on a single 1-based array
+ * of interleaved real and imaginary numbers.  Because we have two
+ * separate zero-based arrays, our indices are quite different.
+ * Here is the correspondence between Num. Rec. indices and our indices:
+ *
+ * i1  <->  real[i]
+ * i2  <->  imag[i]
+ * i3  <->  real[n/2-i]
+ * i4  <->  imag[n/2-i]
+ *)
+procedure RealFFT(NumSamples: integer; RealIn, RealOut, ImagOut: PSingleArray);
+var
+  Half: Integer;
+  i: Integer;
+  theta: Single;
+  tmpReal, tmpImag: PSingleArray;
+  wtemp: Single;
+  wpr, wpi, wr, wi: Single;
+  i3: Integer;
+  h1r, h1i, h2r, h2i: Single;
+begin
+   Half := NumSamples div 2;
+
+   theta := Pi / Half;
+
+   GetMem(tmpReal, Half * sizeof(Single));
+   GetMem(tmpImag, Half * sizeof(Single));
+
+   for i := 0 to Half-1 do
+   begin
+      tmpReal[i] := RealIn[2 * i];
+      tmpImag[i] := RealIn[2 * i + 1];
+   end;
+
+   FFT(Half, false, tmpReal, tmpImag, RealOut, ImagOut);
+
+   wtemp := sin(0.5 * theta);
+
+   wpr := -2.0 * wtemp * wtemp;
+   wpi := sin(theta);
+   wr := 1.0 + wpr;
+   wi := wpi;
+
+   for i := 1 to (Half div 2)-1 do
+   begin
+      i3 := Half - i;
+
+      h1r := 0.5 * (RealOut[i] + RealOut[i3]);
+      h1i := 0.5 * (ImagOut[i] - ImagOut[i3]);
+      h2r := 0.5 * (ImagOut[i] + ImagOut[i3]);
+      h2i := -0.5 * (RealOut[i] - RealOut[i3]);
+
+      RealOut[i] := h1r + wr * h2r - wi * h2i;
+      ImagOut[i] := h1i + wr * h2i + wi * h2r;
+      RealOut[i3] := h1r - wr * h2r + wi * h2i;
+      ImagOut[i3] := -h1i + wr * h2i + wi * h2r;
+
+      wtemp := wr;
+      wr := wtemp * wpr - wi * wpi + wr;
+      wi := wi * wpr + wtemp * wpi + wi;
+   end;
+
+   h1r := RealOut[0];
+   RealOut[0] := h1r + ImagOut[0];
+   ImagOut[0] := h1r - ImagOut[0];
+
+   FreeMem(tmpReal);
+   FreeMem(tmpImag);
+end;
+
+{*
+ * PowerSpectrum
+ *
+ * This function computes the same as RealFFT, above, but
+ * adds the squares of the real and imaginary part of each
+ * coefficient, extracting the power and throwing away the
+ * phase.
+ *
+ * For speed, it does not call RealFFT, but duplicates some
+ * of its code.
+ *}
+procedure PowerSpectrum(NumSamples: Integer; In_, Out_: PSingleArray);
+var
+  Half: Integer;
+  i: Integer;
+  theta: Single;
+  tmpReal, tmpImag, RealOut, ImagOut: PSingleArray;
+  wtemp: Single;
+  wpr, wpi, wr, wi: Single;
+  i3: Integer;
+  h1r, h1i, h2r, h2i, rt, it: Single;
+begin
+   Half := NumSamples div 2;
+
+   theta := Pi / Half;
+
+   GetMem(tmpReal, Half * sizeof(Single));
+   GetMem(tmpImag, Half * sizeof(Single));
+   GetMem(RealOut, Half * sizeof(Single));
+   GetMem(ImagOut, Half * sizeof(Single));
+
+   for i := 0 to Half-1 do begin
+      tmpReal[i] := In_[2 * i];
+      tmpImag[i] := In_[2 * i + 1];
+   end;
+
+   FFT(Half, false, tmpReal, tmpImag, RealOut, ImagOut);
+
+   wtemp := sin(0.5 * theta);
+
+   wpr := -2.0 * wtemp * wtemp;
+   wpi := sin(theta);
+   wr := 1.0 + wpr;
+   wi := wpi;
+
+   for i := 1 to (Half div 2)-1 do
+   begin
+      i3 := Half - i;
+
+      h1r := 0.5 * (RealOut[i] + RealOut[i3]);
+      h1i := 0.5 * (ImagOut[i] - ImagOut[i3]);
+      h2r := 0.5 * (ImagOut[i] + ImagOut[i3]);
+      h2i := -0.5 * (RealOut[i] - RealOut[i3]);
+
+      rt := h1r + wr * h2r - wi * h2i;
+      it := h1i + wr * h2i + wi * h2r;
+
+      Out_[i] := rt * rt + it * it;
+
+      rt := h1r - wr * h2r + wi * h2i;
+      it := -h1i + wr * h2i + wi * h2r;
+
+      Out_[i3] := rt * rt + it * it;
+
+      wtemp := wr;
+      wr := wtemp * wpr - wi * wpi + wr;
+      wi := wi * wpr + wtemp * wpi + wi;
+   end;
+
+   h1r := RealOut[0];
+   rt := h1r + ImagOut[0];
+   it := h1r - ImagOut[0];
+   Out_[0] := rt * rt + it * it;
+
+   rt := RealOut[Half div 2];
+   it := ImagOut[Half div 2];
+   Out_[Half div 2] := rt * rt + it * it;
+
+   FreeMem(tmpReal);
+   FreeMem(tmpImag);
+   FreeMem(RealOut);
+   FreeMem(ImagOut);
+end;
+
+(*
+ * Windowing Functions
+ *)
+function NumWindowFuncs(): integer;
+begin
+  Result := Length(FFTWindowName);
+end;
+
+function WindowFuncName(whichFunction: TFFTWindowFunc): string;
+begin
+  Result := FFTWindowName[whichFunction];
+end;
+
+procedure WindowFunc(whichFunction: TFFTWindowFunc; NumSamples: Integer; in_: PSingleArray);
+var
+  i: Integer;
+  A: Single;
+begin
+  case whichFunction of
+    fwfBartlett:
+    begin
+      // Bartlett (triangular) window
+      for i := 0 to (NumSamples div 2)-1 do
+      begin
+        in_[i] := in_[i] * (i / (NumSamples / 2));
+        in_[i + (NumSamples div 2)] :=
+            in_[i + (NumSamples div 2)] *
+            (1.0 - (i / (NumSamples / 2)));
+      end;
+    end;
+    fwfHamming:
+    begin
+      // Hamming
+      for i := 0 to NumSamples-1 do
+      begin
+        in_[i] := in_[i] * (0.54 - 0.46 * cos(2 * Pi * i / (NumSamples - 1)));
+      end;
+    end;
+    fwfHanning:
+    begin
+      // Hanning
+      for i := 0 to NumSamples-1 do
+      begin
+        in_[i] := in_[i] * (0.50 - 0.50 * cos(2 * Pi * i / (NumSamples - 1)));
+      end;
+    end;
+    fwfBlackman:
+    begin
+      // Blackman
+      for i := 0 to NumSamples-1 do
+      begin
+        in_[i] := in_[i] * (0.42 - 0.5 * cos (2 * Pi * i / (NumSamples - 1)) + 0.08 * cos (4 * Pi * i / (NumSamples - 1)));
+      end;
+    end;
+    fwfBlackman_Harris:
+    begin
+      // Blackman-Harris
+      for i := 0 to NumSamples-1 do
+      begin
+        in_[i] := in_[i] * (0.35875 - 0.48829 * cos(2 * Pi * i /(NumSamples-1)) + 0.14128 * cos(4 * Pi * i/(NumSamples-1)) - 0.01168 * cos(6 * Pi * i/(NumSamples-1)));
+      end;
+    end;
+    fwfWelch:
+    begin
+      // Welch
+      for i := 0 to NumSamples-1 do
+      begin
+        in_[i] := in_[i] * 4*i/NumSamples*(1-(i/NumSamples));
+      end;
+    end;
+    fwfGaussian2_5:
+    begin
+      // Gaussian (a=2.5)
+      // Precalculate some values, and simplify the fmla to try and reduce overhead
+      A := -2*2.5*2.5;
+
+      for i := 0 to NumSamples-1 do
+      begin
+        // full
+        // in_[i] := in_[i] * exp(-0.5*(A*((i-NumSamples/2)/NumSamples/2))*(A*((i-NumSamples/2)/NumSamples/2)));
+        // reduced
+        //in_[i] := in_[i] * exp(A*(0.25 + ((i/NumSamples)*(i/NumSamples)) - (i/NumSamples)));
+      end;
+    end;
+    fwfGaussian3_5:
+    begin
+      // Gaussian (a=3.5)
+      A := -2*3.5*3.5;
+
+      for i := 0 to NumSamples-1 do
+      begin
+        // reduced
+        in_[i] := in_[i] * exp(A*(0.25 + ((i/NumSamples)*(i/NumSamples)) - (i/NumSamples)));
+      end;
+    end;
+    fwfGaussian4_5:
+    begin
+      // Gaussian (a=4.5)
+      A := -2*4.5*4.5;
+
+      for i := 0 to NumSamples-1 do
+      begin
+        // reduced
+        in_[i] := in_[i] * exp(A*(0.25 + ((i/NumSamples)*(i/NumSamples)) - (i/NumSamples)));
+      end;
+    end;
+  end;
+end;
+
+end.