/* Calculates correlation matrix X'*X */
void silk_corrMatrix_FLP(const silk_float * x, /* I x vector [ L+order-1 ] used to create X */
const int L, /* I Length of vectors */
const int Order, /* I Max lag for correlation */
silk_float * XX /* O X'*X correlation matrix [order x order] */
)
{
int j, lag;
double energy;
const silk_float *ptr1, *ptr2;
ptr1 = &x[Order - 1]; /* First sample of column 0 of X */
energy = silk_energy_FLP(ptr1, L); /* X[:,0]'*X[:,0] */
matrix_ptr(XX, 0, 0, Order) = (silk_float) energy;
for (j = 1; j < Order; j++) {
/* Calculate X[:,j]'*X[:,j] */
energy += ptr1[-j] * ptr1[-j] - ptr1[L - j] * ptr1[L - j];
matrix_ptr(XX, j, j, Order) = (silk_float) energy;
}
ptr2 = &x[Order - 2]; /* First sample of column 1 of X */
for (lag = 1; lag < Order; lag++) {
/* Calculate X[:,0]'*X[:,lag] */
energy = silk_inner_product_FLP(ptr1, ptr2, L);
matrix_ptr(XX, lag, 0, Order) = (silk_float) energy;
matrix_ptr(XX, 0, lag, Order) = (silk_float) energy;
/* Calculate X[:,j]'*X[:,j + lag] */
for (j = 1; j < (Order - lag); j++) {
energy +=
ptr1[-j] * ptr2[-j] - ptr1[L - j] * ptr2[L - j];
matrix_ptr(XX, lag + j, j, Order) = (silk_float) energy;
matrix_ptr(XX, j, lag + j, Order) = (silk_float) energy;
}
ptr2--; /* Next column of X */
}
}
/* compute autocorrelation */
void silk_autocorrelation_FLP(
SKP_float *results, /* O result (length correlationCount) */
const SKP_float *inputData, /* I input data to correlate */
SKP_int inputDataSize, /* I length of input */
SKP_int correlationCount /* I number of correlation taps to compute */
)
{
SKP_int i;
if ( correlationCount > inputDataSize ) {
correlationCount = inputDataSize;
}
for( i = 0; i < correlationCount; i++ ) {
results[ i ] = (SKP_float)silk_inner_product_FLP( inputData, inputData + i, inputDataSize - i );
}
}
/* Calculates correlation vector X'*t */
void silk_corrVector_FLP(const silk_float * x, /* I x vector [L+order-1] used to create X */
const silk_float * t, /* I Target vector [L] */
const int L, /* I Length of vecors */
const int Order, /* I Max lag for correlation */
silk_float * Xt /* O X'*t correlation vector [order] */
)
{
int lag;
const silk_float *ptr1;
ptr1 = &x[Order - 1]; /* Points to first sample of column 0 of X: X[:,0] */
for (lag = 0; lag < Order; lag++) {
/* Calculate X[:,lag]'*t */
Xt[lag] = (silk_float) silk_inner_product_FLP(ptr1, t, L);
ptr1--; /* Next column of X */
}
}
/* Compute reflection coefficients from input signal */
silk_float silk_burg_modified_FLP( /* O returns residual energy */
silk_float A[], /* O prediction coefficients (length order) */
const silk_float x[], /* I input signal, length: nb_subfr*(D+L_sub) */
const silk_float minInvGain, /* I minimum inverse prediction gain */
const opus_int subfr_length, /* I input signal subframe length (incl. D preceding samples) */
const opus_int nb_subfr, /* I number of subframes stacked in x */
const opus_int D /* I order */
)
{
opus_int k, n, s, reached_max_gain;
double C0, invGain, num, nrg_f, nrg_b, rc, Atmp, tmp1, tmp2;
const silk_float *x_ptr;
double C_first_row[ SILK_MAX_ORDER_LPC ], C_last_row[ SILK_MAX_ORDER_LPC ];
double CAf[ SILK_MAX_ORDER_LPC + 1 ], CAb[ SILK_MAX_ORDER_LPC + 1 ];
double Af[ SILK_MAX_ORDER_LPC ];
silk_assert( subfr_length * nb_subfr <= MAX_FRAME_SIZE );
/* Compute autocorrelations, added over subframes */
C0 = silk_energy_FLP( x, nb_subfr * subfr_length );
silk_memset( C_first_row, 0, SILK_MAX_ORDER_LPC * sizeof( double ) );
for( s = 0; s < nb_subfr; s++ ) {
x_ptr = x + s * subfr_length;
for( n = 1; n < D + 1; n++ ) {
C_first_row[ n - 1 ] += silk_inner_product_FLP( x_ptr, x_ptr + n, subfr_length - n );
}
}
silk_memcpy( C_last_row, C_first_row, SILK_MAX_ORDER_LPC * sizeof( double ) );
/* Initialize */
CAb[ 0 ] = CAf[ 0 ] = C0 + FIND_LPC_COND_FAC * C0 + 1e-9f;
invGain = 1.0f;
reached_max_gain = 0;
for( n = 0; n < D; n++ ) {
/* Update first row of correlation matrix (without first element) */
/* Update last row of correlation matrix (without last element, stored in reversed order) */
/* Update C * Af */
/* Update C * flipud(Af) (stored in reversed order) */
for( s = 0; s < nb_subfr; s++ ) {
x_ptr = x + s * subfr_length;
tmp1 = x_ptr[ n ];
tmp2 = x_ptr[ subfr_length - n - 1 ];
for( k = 0; k < n; k++ ) {
C_first_row[ k ] -= x_ptr[ n ] * x_ptr[ n - k - 1 ];
C_last_row[ k ] -= x_ptr[ subfr_length - n - 1 ] * x_ptr[ subfr_length - n + k ];
Atmp = Af[ k ];
tmp1 += x_ptr[ n - k - 1 ] * Atmp;
tmp2 += x_ptr[ subfr_length - n + k ] * Atmp;
}
for( k = 0; k <= n; k++ ) {
CAf[ k ] -= tmp1 * x_ptr[ n - k ];
CAb[ k ] -= tmp2 * x_ptr[ subfr_length - n + k - 1 ];
}
}
tmp1 = C_first_row[ n ];
tmp2 = C_last_row[ n ];
for( k = 0; k < n; k++ ) {
Atmp = Af[ k ];
tmp1 += C_last_row[ n - k - 1 ] * Atmp;
tmp2 += C_first_row[ n - k - 1 ] * Atmp;
}
CAf[ n + 1 ] = tmp1;
CAb[ n + 1 ] = tmp2;
/* Calculate nominator and denominator for the next order reflection (parcor) coefficient */
num = CAb[ n + 1 ];
nrg_b = CAb[ 0 ];
nrg_f = CAf[ 0 ];
for( k = 0; k < n; k++ ) {
Atmp = Af[ k ];
num += CAb[ n - k ] * Atmp;
nrg_b += CAb[ k + 1 ] * Atmp;
nrg_f += CAf[ k + 1 ] * Atmp;
}
silk_assert( nrg_f > 0.0 );
silk_assert( nrg_b > 0.0 );
/* Calculate the next order reflection (parcor) coefficient */
rc = -2.0 * num / ( nrg_f + nrg_b );
silk_assert( rc > -1.0 && rc < 1.0 );
/* Update inverse prediction gain */
tmp1 = invGain * ( 1.0 - rc * rc );
if( tmp1 <= minInvGain ) {
/* Max prediction gain exceeded; set reflection coefficient such that max prediction gain is exactly hit */
rc = sqrt( 1.0 - minInvGain / invGain );
if( num > 0 ) {
/* Ensure adjusted reflection coefficients has the original sign */
rc = -rc;
}
invGain = minInvGain;
reached_max_gain = 1;
} else {
invGain = tmp1;
}
/* Update the AR coefficients */
for( k = 0; k < (n + 1) >> 1; k++ ) {
tmp1 = Af[ k ];
tmp2 = Af[ n - k - 1 ];
Af[ k ] = tmp1 + rc * tmp2;
Af[ n - k - 1 ] = tmp2 + rc * tmp1;
}
Af[ n ] = rc;
if( reached_max_gain ) {
/* Reached max prediction gain; set remaining coefficients to zero and exit loop */
for( k = n + 1; k < D; k++ ) {
Af[ k ] = 0.0;
}
break;
}
/* Update C * Af and C * Ab */
for( k = 0; k <= n + 1; k++ ) {
tmp1 = CAf[ k ];
CAf[ k ] += rc * CAb[ n - k + 1 ];
CAb[ n - k + 1 ] += rc * tmp1;
}
}
if( reached_max_gain ) {
/* Convert to silk_float */
for( k = 0; k < D; k++ ) {
A[ k ] = (silk_float)( -Af[ k ] );
}
/* Subtract energy of preceding samples from C0 */
for( s = 0; s < nb_subfr; s++ ) {
C0 -= silk_energy_FLP( x + s * subfr_length, D );
}
/* Approximate residual energy */
nrg_f = C0 * invGain;
} else {
/* Compute residual energy and store coefficients as silk_float */
nrg_f = CAf[ 0 ];
tmp1 = 1.0;
for( k = 0; k < D; k++ ) {
Atmp = Af[ k ];
nrg_f += CAf[ k + 1 ] * Atmp;
tmp1 += Atmp * Atmp;
A[ k ] = (silk_float)(-Atmp);
}
nrg_f -= FIND_LPC_COND_FAC * C0 * tmp1;
}
/* Return residual energy */
return (silk_float)nrg_f;
}
/* Compute reflection coefficients from input signal */
silk_float silk_burg_modified_FLP( /* O returns residual energy */
silk_float A[], /* O prediction coefficients (length order) */
const silk_float x[], /* I input signal, length: nb_subfr*(D+L_sub) */
const opus_int subfr_length, /* I input signal subframe length (incl. D preceeding samples) */
const opus_int nb_subfr, /* I number of subframes stacked in x */
const silk_float WhiteNoiseFrac, /* I fraction added to zero-lag autocorrelation */
const opus_int D /* I order */
)
{
opus_int k, n, s;
double C0, num, nrg_f, nrg_b, rc, Atmp, tmp1, tmp2;
const silk_float *x_ptr;
double C_first_row[ SILK_MAX_ORDER_LPC ], C_last_row[ SILK_MAX_ORDER_LPC ];
double CAf[ SILK_MAX_ORDER_LPC + 1 ], CAb[ SILK_MAX_ORDER_LPC + 1 ];
double Af[ SILK_MAX_ORDER_LPC ];
silk_assert( subfr_length * nb_subfr <= MAX_FRAME_SIZE );
silk_assert( nb_subfr <= MAX_NB_SUBFR );
/* Compute autocorrelations, added over subframes */
C0 = silk_energy_FLP( x, nb_subfr * subfr_length );
silk_memset( C_first_row, 0, SILK_MAX_ORDER_LPC * sizeof( double ) );
for( s = 0; s < nb_subfr; s++ ) {
x_ptr = x + s * subfr_length;
for( n = 1; n < D + 1; n++ ) {
C_first_row[ n - 1 ] += silk_inner_product_FLP( x_ptr, x_ptr + n, subfr_length - n );
}
}
silk_memcpy( C_last_row, C_first_row, SILK_MAX_ORDER_LPC * sizeof( double ) );
/* Initialize */
CAb[ 0 ] = CAf[ 0 ] = C0 + WhiteNoiseFrac * C0 + 1e-9f;
for( n = 0; n < D; n++ ) {
/* Update first row of correlation matrix (without first element) */
/* Update last row of correlation matrix (without last element, stored in reversed order) */
/* Update C * Af */
/* Update C * flipud(Af) (stored in reversed order) */
for( s = 0; s < nb_subfr; s++ ) {
x_ptr = x + s * subfr_length;
tmp1 = x_ptr[ n ];
tmp2 = x_ptr[ subfr_length - n - 1 ];
for( k = 0; k < n; k++ ) {
C_first_row[ k ] -= x_ptr[ n ] * x_ptr[ n - k - 1 ];
C_last_row[ k ] -= x_ptr[ subfr_length - n - 1 ] * x_ptr[ subfr_length - n + k ];
Atmp = Af[ k ];
tmp1 += x_ptr[ n - k - 1 ] * Atmp;
tmp2 += x_ptr[ subfr_length - n + k ] * Atmp;
}
for( k = 0; k <= n; k++ ) {
CAf[ k ] -= tmp1 * x_ptr[ n - k ];
CAb[ k ] -= tmp2 * x_ptr[ subfr_length - n + k - 1 ];
}
}
tmp1 = C_first_row[ n ];
tmp2 = C_last_row[ n ];
for( k = 0; k < n; k++ ) {
Atmp = Af[ k ];
tmp1 += C_last_row[ n - k - 1 ] * Atmp;
tmp2 += C_first_row[ n - k - 1 ] * Atmp;
}
CAf[ n + 1 ] = tmp1;
CAb[ n + 1 ] = tmp2;
/* Calculate nominator and denominator for the next order reflection (parcor) coefficient */
num = CAb[ n + 1 ];
nrg_b = CAb[ 0 ];
nrg_f = CAf[ 0 ];
for( k = 0; k < n; k++ ) {
Atmp = Af[ k ];
num += CAb[ n - k ] * Atmp;
nrg_b += CAb[ k + 1 ] * Atmp;
nrg_f += CAf[ k + 1 ] * Atmp;
}
silk_assert( nrg_f > 0.0 );
silk_assert( nrg_b > 0.0 );
/* Calculate the next order reflection (parcor) coefficient */
rc = -2.0 * num / ( nrg_f + nrg_b );
silk_assert( rc > -1.0 && rc < 1.0 );
/* Update the AR coefficients */
for( k = 0; k < (n + 1) >> 1; k++ ) {
tmp1 = Af[ k ];
tmp2 = Af[ n - k - 1 ];
Af[ k ] = tmp1 + rc * tmp2;
Af[ n - k - 1 ] = tmp2 + rc * tmp1;
}
Af[ n ] = rc;
/* Update C * Af and C * Ab */
for( k = 0; k <= n + 1; k++ ) {
tmp1 = CAf[ k ];
CAf[ k ] += rc * CAb[ n - k + 1 ];
CAb[ n - k + 1 ] += rc * tmp1;
}
}
/* Return residual energy */
nrg_f = CAf[ 0 ];
tmp1 = 1.0;
for( k = 0; k < D; k++ ) {
Atmp = Af[ k ];
nrg_f += CAf[ k + 1 ] * Atmp;
tmp1 += Atmp * Atmp;
A[ k ] = (silk_float)(-Atmp);
}
nrg_f -= WhiteNoiseFrac * C0 * tmp1;
return (silk_float)nrg_f;
}
