/* Levinson recursion for linear prediction (LP) */
void LPFormantTracker::levinson(dtype * R, dtype * aa, const int size) {
dtype ki, t;
dtype E = R[0];
int i0, j0;
if (R[0] == 0.0) {
for(i0=1; i0<size; i0++)
aa[i0] = 0.0;
aa[0] = 1;
return;
}
for(i0=1; i0<size; i0++) {
ki = R[i0];
// Update reflection coefficient:
for (j0=1; j0<i0; j0++)
ki += aa[j0] * R[i0-j0];
ki /= -E;
E *= (1 - ki*ki);
// Update polynomial:
for (j0 = 1; j0 <= RSL(i0 - 1, 1); j0++) {
t = aa[j0];
aa[j0] += ki * aa[i0 - j0];
aa[i0 - j0] += ki * t;
}
if (i0%2 == 0) aa[RSL(i0, 1)] *= 1+ki;
// Record reflection coefficient
aa[i0] = ki;
} // end of for loop
aa[0] = 1.0;
}
static void rsem_AA3(void)
{
WSL(X1);
RSL(X2); // this one should fail
}
static void rsem_AA2(void)
{
RSL(X1);
WSL(X2); // this one should fail
}
static void rsem_AA1(void)
{
RSL(X1);
RSL(X1); // this one should fail
}
static void rsem_AA3(void)
{
WSL(X1);
RSL(X2);
}
static void rsem_AA2(void)
{
RSL(X1);
WSL(X2);
}
static void rsem_AA1B(void)
{
RSL(X1);
RSL(X2);
}
static void rsem_AA1(void)
{
RSL(X1);
RSL(X1);
}
