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#ifdef HAVE_CONFIG_H
#include "config.h"
#endif
#include <math.h>
#include <string.h>
#include <assert.h>
#include "arch.h"
#include "burg.h"
#define MAX_FRAME_SIZE 384 /* subfr_length * nb_subfr = ( 0.005 * 16000 + 16 ) * 4 = 384*/
#define SILK_MAX_ORDER_LPC 16
#define FIND_LPC_COND_FAC 1e-5f
/* sum of squares of a silk_float array, with result as double */
static double silk_energy_FLP(
const float *data,
int dataSize
)
{
int i;
double result;
/* 4x unrolled loop */
result = 0.0;
for( i = 0; i < dataSize - 3; i += 4 ) {
result += data[ i + 0 ] * (double)data[ i + 0 ] +
data[ i + 1 ] * (double)data[ i + 1 ] +
data[ i + 2 ] * (double)data[ i + 2 ] +
data[ i + 3 ] * (double)data[ i + 3 ];
}
/* add any remaining products */
for( ; i < dataSize; i++ ) {
result += data[ i ] * (double)data[ i ];
}
assert( result >= 0.0 );
return result;
}
/* inner product of two silk_float arrays, with result as double */
static double silk_inner_product_FLP(
const float *data1,
const float *data2,
int dataSize
)
{
int i;
double result;
/* 4x unrolled loop */
result = 0.0;
for( i = 0; i < dataSize - 3; i += 4 ) {
result += data1[ i + 0 ] * (double)data2[ i + 0 ] +
data1[ i + 1 ] * (double)data2[ i + 1 ] +
data1[ i + 2 ] * (double)data2[ i + 2 ] +
data1[ i + 3 ] * (double)data2[ i + 3 ];
}
/* add any remaining products */
for( ; i < dataSize; i++ ) {
result += data1[ i ] * (double)data2[ i ];
}
return result;
}
/* Compute reflection coefficients from input signal */
float silk_burg_analysis( /* O returns residual energy */
float A[], /* O prediction coefficients (length order) */
const float x[], /* I input signal, length: nb_subfr*(D+L_sub) */
const float minInvGain, /* I minimum inverse prediction gain */
const int subfr_length, /* I input signal subframe length (incl. D preceding samples) */
const int nb_subfr, /* I number of subframes stacked in x */
const int D /* I order */
)
{
int k, n, s, reached_max_gain;
double C0, invGain, num, nrg_f, nrg_b, rc, Atmp, tmp1, tmp2;
const 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 ];
assert( subfr_length * nb_subfr <= MAX_FRAME_SIZE );
/* Compute autocorrelations, added over subframes */
C0 = silk_energy_FLP( x, nb_subfr * subfr_length );
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 );
}
}
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;
}
assert( nrg_f > 0.0 );
assert( nrg_b > 0.0 );
/* Calculate the next order reflection (parcor) coefficient */
rc = -2.0 * num / ( nrg_f + nrg_b );
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 float */
for( k = 0; k < D; k++ ) {
A[ k ] = (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 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 ] = (float)(-Atmp);
}
nrg_f -= FIND_LPC_COND_FAC * C0 * tmp1;
}
/* Return residual energy */
return MAX32(0, (float)nrg_f);
}