phi_lsfr.c

MPEG-4编解码的实现(包括MPEG4视音频编解码)

*   polynomials are formed from the error filter polynomial,
*     Fa(D) = A(D) + D**(N+1) A(D**(-1))  (N+2 terms, symmetric)
*     Fb(D) = A(D) - D**(N+1) A(D**(-1))  (N+2 terms, anti-symmetric)
*/
  fa[0] = (float)1.0;
  for (i = 1, j = np; i < na; ++i, --j)
    fa[i] = pc[i] + pc[j];

  fb[0] = (float)1.0;
  for (i = 1, j = np; i < nb; ++i, --j)
    fb[i] = pc[i] - pc[j];

/*
* N even,  Fa(D)  includes a factor 1+D,
*          Fb(D)  includes a factor 1-D
* N odd,   Fb(D)  includes a factor 1-D**2
* Divide out these factors, leaving even order symmetric polynomials, M is the
* total number of terms and Nc is the number of unique terms,
*
*   N       polynomial            M         Nc=(M+1)/2
* even,  Ga(D) = F1(D)/(1+D)     N+1          N/2+1
*        Gb(D) = F2(D)/(1-D)     N+1          N/2+1
* odd,   Ga(D) = F1(D)           N+2        (N+1)/2+1
*        Gb(D) = F2(D)/(1-D**2)   N          (N+1)/2
*/
  if (odd) {
    for (i = 2; i < nb; ++i)
      fb[i] = fb[i] + fb[i-2];
  }
  else {
    for (i = 1; i < na; ++i) {
      fa[i] = fa[i] - fa[i-1];
      fb[i] = fb[i] + fb[i-1];
    }
  }

/*
*   To look for roots on the unit circle, Ga(D) and Gb(D) are evaluated for
*   D=exp(jw).  Since Gz(D) and Gb(D) are symmetric, they can be expressed in
*   terms of a series in cos(nw) for D on the unit circle.  Since M is odd and
*   D=exp(jw)
*
*           M-1        n 
*   Ga(D) = SUM fa(n) D             (symmetric, fa(n) = fa(M-1-n))
*           n=0
*                                    Mh-1
*         = exp(j Mh w) [ f1(Mh) + 2 SUM fa(n) cos((Mh-n)w) ]
*                                    n=0
*                       Mh
*         = exp(j Mh w) SUM ta(n) cos(nw) ,
*                       n=0
*
*   where Mh=(M-1)/2=Nc-1.  The Nc=Mh+1 coefficients ta(n) are defined as
*
*   ta(n) =   fa(Nc-1) ,    n=0,
*         = 2 fa(Nc-1-n) ,  n=1,...,Nc-1.
*   The next step is to identify cos(nw) with the Chebyshev polynomial T(n,x).
*   The Chebyshev polynomials satisfy the relationship T(n,cos(w)) = cos(nw).
*   Omitting the exponential delay term, the series expansion in terms of
*   Chebyshev polynomials is
*
*           Nc-1
*   Ta(x) = SUM ta(n) T(n,x)
*           n=0
*
*   The domain of Ta(x) is -1 < x < +1.  For a given root of Ta(x), say x0,
*   the corresponding position of the root of Fa(D) on the unit circle is
*   exp(j arccos(x0)).
*/
  ta[0] = fa[na-1];
  for (i = 1, j = na - 2; i < na; ++i, --j)
    ta[i] = (float)2.0 * fa[j];

  tb[0] = fb[nb-1];
  for (i = 1, j = nb - 2; i < nb; ++i, --j)
    tb[i] = (float)2.0 * fb[j];

/*
*   To find the roots, we sample the polynomials Ta(x) and Tb(x) looking for
*   sign changes.  An interval containing a root is successively bisected to
*   narrow the interval and then linear interpolation is used to estimate the
*   root.  For a given root at x0, the line spectral frequency is w0=acos(x0).
*
*   Since the roots of the two polynomials interlace, the search for roots
*   alternates between the polynomials Ta(x) and Tb(x).  The sampling interval
*   must be small enough to avoid having two cancelling sign changes in the
*   same interval.  Consider specifying the resolution in the LSF domain.  For
*   an interval [xl, xh], the corresponding interval in frequency is [w1, w2],
*   with xh=cos(w1) and xl=cos(w2) (note the reversal in order).  Let dw=w2-w1,
*     dx = xh-xl = xh [1-cos(dw)] + sqrt(1-xh*xh) sin(dw).
*   However, the calculation of the square root is overly time-consuming.  If
*   instead, we use equal steps in the x-domain, the resolution in the LSF
*   domain is best at at pi/2 and worst at 0 and pi.  As a compromise we fit a
*   quadratic to the step size relationship.  At x=1, dx=(1-cos(dw); at x=0,
*   dx=sin(dw).  Then the approximation is
*     dx' = (a(1-cos(dw))-sin(dw)) x**2 + sin(dw)).
*   For a=1, this value underestimates the step size in the range of interest.
*   However, the step size for just below x=1 and just above x=-1 fall well
*   below the desired step sizes.  To compensate for this, we use a=4.  Then at
*   x=+1 and x=-1, the step sizes are too large by a factor of 4, but rapidly
*   fall to about 60% of the desired values and then rise slowly to become 
*   equal to the desired step size at x=0.
*/
  nf = 0;
  t = ta;
  n = na;
  xroot = (float)2.0;
  xlow = (float)1.0;
  ylow = FNevChebP(xlow, t, n);

/*
*   Define the step-size function parameters
*   The resolution in the LSF domain is at least DW/2**NBIS, not counting the
*   increase in resolution due to the linear interpolation step.  For
*   DW=0.02*Pi, and NBIS=4, and a sampling frequency of 8000, this corresponds
*   to 5 Hz.
*/
  ss = (float)sin (DW);
  aa = (float)(4.0 - 4.0 * cos (DW)  - (double)ss);

/* Root search loop */
  while (xlow > (float)-1.0 && nf < np) {

    /* New trial point */
    xhigh = xlow;
    yhigh = ylow;
    dx = aa * xhigh * xhigh + ss;
    xlow = xhigh - dx;
    if (xlow < (float)-1.0)
      xlow = (float)-1.0;
    ylow = FNevChebP(xlow, t, n);
    if (ylow * yhigh <= (float)0.0) {

    /* Bisections of the interval containing a sign change */
      dx = xhigh - xlow;
      for (i = 1; i <= NBIS; ++i) {
	dx = (float)0.5 * dx;
	xmid = xlow + dx;
	ymid = FNevChebP(xmid, t, n);
	if (ylow * ymid <= (float)0.0) {
	  yhigh = ymid;
	  xhigh = xmid;
	}
	else {
	  ylow = ymid;
	  xlow = xmid;
	}
      }

      /*
       * Linear interpolation in the subinterval with a sign change
       * (take care if yhigh=ylow=0)
       */
      if (yhigh != ylow)
	xmid = xlow + dx * ylow / (ylow - yhigh);
      else
	xmid = xlow + dx;

      /* New root position */
      lsf[nf] = (float)acos((double) xmid);
      ++nf;

      /* Start the search for the roots of the next polynomial at the estimated
       * location of the root just found.  We have to catch the case that the
       * two polynomials have roots at the same place to avoid getting stuck at
       * that root.
       */
      if (xmid >= xroot) {
	xmid = xlow - dx;
      }
      xroot = xmid;
      if (t == ta) {
	t = tb;
	n = nb;
      }
      else {
	t = ta;
	n = na;
      }
      xlow = xmid;
      ylow = FNevChebP(xlow, t, n);
    }
  }

/* Error if np roots have not been found */
  if (nf != np) {
    printf("\nWARNING: pc2lsf failed to find all lsf nf=%ld np=%ld\n", nf, np);
    return(0);
  }
  return(1);
}

/**---------------------------------------------------------------------------- Telecommunications & Signal Processing Lab ---------------
*                             McGill University
*
* Module:
*  float FNevChebP(float x,  float c[], long N)
*
* Purpose:
*  Evaluate a series expansion in Chebyshev polynomials
*
* Description:
*  The series expansion in Chebyshev polynomials is defined as
*
*              N-1
*       Y(x) = SUM c(i) T(i,x) ,
*              i=0
*
*  where N is the order of the expansion, c(i) is the coefficient for the i'th
*  Chebyshev polynomial, and T(i,x) is the i'th order Chebyshev polynomial
*  evaluated at x.
*
*  The Chebyshev polynomials satisfy the recursion
*    T(i,x) = 2x T(i-1,x) - T(i-2,x),
*  with the initial conditions T(0,x)=1 and T(1,x)=x.  This routine evaluates
*  the expansion using a backward recursion.
*
* Parameters:
*  <-  float
*  FNevChebP -	Resulting value
*  ->  float
*  x -		Input value
*  ->  float []
*  c -		Array of coefficient values.  c[i] is the coefficient of the
*		i'th order Chebyshev polynomial.
*  ->  long
*  N -		Number of coefficients
*
* Author / revision:
*  P. Kabal
*  $Revision: 1.2 $  $Date: 2002/05/13 15:52:07 $
*
-------------------------------------------------------------------------*/
float FNevChebP(		/* result */
float  x,			/* input : value */
const float c[],		/* input : Array of coefficient values */
long n				/* input : Number of coefficients */
)
{
  long i;
  float b0, b1, b2;

/*****************
*   Consider the backward recursion
*     b(i,x) = 2xb(i+1,x) - b(i+2,x) + c(i),
*   with initial conditions b(n,x)=0 and b(n+1,x)=0.  Then dropping the
*   dependence on x,
*     c(i) = b(i) - 2xb(i+1) + b(i+2).
*
*          n-1
*   Y(x) = SUM c(i) T(i)
*          i=0
*
*          n-1
*        = SUM [b(i)-2xb(i+1)+b(i+2)] T(i)
*          i=0
*                                             n-1
*        = b(0)T(0) + b(1)T(1) - 2xb(1)T(0) + SUM b(i) [T(i)-2xT(i-1)+T(i-2)] 
*                                             i=2
* The term inside the sum is zero because of the recursive relationship
* satisfied by the Chebyshev polynomials.  Then substituting the values T(0)=1
* and T(1)=x, Y(x) is expressed in terms of the diff. between b(0) and b(2)
* (errors in b(0) and b(2) tend to cancel),
*   Y(x) = b(0)-xb(1) = [b(0)-b(2)+c(0)] / 2
*******************
*/
  b1 = (float)0.0;
  b0 = (float)0.0;
  for (i = n - 1; i >= 0; --i) {
    b2 = b1;
    b1 = b0;
    b0 = (float)2.0 * x * b1 - b2 + c[i];
  }

  return ((float)0.5 * (b0 - b2 + c[0]));
}