ref: 34da05b30d4947b54c1aafb2dbca8e91f58afaf0
dir: /silk/NLSF2A.c/
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#ifdef HAVE_CONFIG_H
#include "config.h"
#endif
/* conversion between prediction filter coefficients and LSFs */
/* order should be even */
/* a piecewise linear approximation maps LSF <-> cos(LSF) */
/* therefore the result is not accurate LSFs, but the two */
/* functions are accurate inverses of each other */
#include "SigProc_FIX.h"
#include "tables.h"
#define QA 16
/* helper function for NLSF2A(..) */
static OPUS_INLINE void silk_NLSF2A_find_poly(
opus_int32 *out, /* O intermediate polynomial, QA [dd+1] */
const opus_int32 *cLSF, /* I vector of interleaved 2*cos(LSFs), QA [d] */
opus_int dd /* I polynomial order (= 1/2 * filter order) */
)
{
opus_int k, n;
opus_int32 ftmp;
out[0] = silk_LSHIFT( 1, QA );
out[1] = -cLSF[0];
for( k = 1; k < dd; k++ ) {
ftmp = cLSF[2*k]; /* QA*/
out[k+1] = silk_LSHIFT( out[k-1], 1 ) - (opus_int32)silk_RSHIFT_ROUND64( silk_SMULL( ftmp, out[k] ), QA );
for( n = k; n > 1; n-- ) {
out[n] += out[n-2] - (opus_int32)silk_RSHIFT_ROUND64( silk_SMULL( ftmp, out[n-1] ), QA );
}
out[1] -= ftmp;
}
}
/* compute whitening filter coefficients from normalized line spectral frequencies */
void silk_NLSF2A(
opus_int16 *a_Q12, /* O monic whitening filter coefficients in Q12, [ d ] */
const opus_int16 *NLSF, /* I normalized line spectral frequencies in Q15, [ d ] */
const opus_int d /* I filter order (should be even) */
)
{
/* This ordering was found to maximize quality. It improves numerical accuracy of
silk_NLSF2A_find_poly() compared to "standard" ordering. */
static const unsigned char ordering16[16] = {
0, 15, 8, 7, 4, 11, 12, 3, 2, 13, 10, 5, 6, 9, 14, 1
};
static const unsigned char ordering10[10] = {
0, 9, 6, 3, 4, 5, 8, 1, 2, 7
};
const unsigned char *ordering;
opus_int k, i, dd;
opus_int32 cos_LSF_QA[ SILK_MAX_ORDER_LPC ];
opus_int32 P[ SILK_MAX_ORDER_LPC / 2 + 1 ], Q[ SILK_MAX_ORDER_LPC / 2 + 1 ];
opus_int32 Ptmp, Qtmp, f_int, f_frac, cos_val, delta;
opus_int32 a32_QA1[ SILK_MAX_ORDER_LPC ];
opus_int32 maxabs, absval, idx=0, sc_Q16;
silk_assert( LSF_COS_TAB_SZ_FIX == 128 );
silk_assert( d==10||d==16 );
/* convert LSFs to 2*cos(LSF), using piecewise linear curve from table */
ordering = d == 16 ? ordering16 : ordering10;
for( k = 0; k < d; k++ ) {
silk_assert(NLSF[k] >= 0 );
/* f_int on a scale 0-127 (rounded down) */
f_int = silk_RSHIFT( NLSF[k], 15 - 7 );
/* f_frac, range: 0..255 */
f_frac = NLSF[k] - silk_LSHIFT( f_int, 15 - 7 );
silk_assert(f_int >= 0);
silk_assert(f_int < LSF_COS_TAB_SZ_FIX );
/* Read start and end value from table */
cos_val = silk_LSFCosTab_FIX_Q12[ f_int ]; /* Q12 */
delta = silk_LSFCosTab_FIX_Q12[ f_int + 1 ] - cos_val; /* Q12, with a range of 0..200 */
/* Linear interpolation */
cos_LSF_QA[ordering[k]] = silk_RSHIFT_ROUND( silk_LSHIFT( cos_val, 8 ) + silk_MUL( delta, f_frac ), 20 - QA ); /* QA */
}
dd = silk_RSHIFT( d, 1 );
/* generate even and odd polynomials using convolution */
silk_NLSF2A_find_poly( P, &cos_LSF_QA[ 0 ], dd );
silk_NLSF2A_find_poly( Q, &cos_LSF_QA[ 1 ], dd );
/* convert even and odd polynomials to opus_int32 Q12 filter coefs */
for( k = 0; k < dd; k++ ) {
Ptmp = P[ k+1 ] + P[ k ];
Qtmp = Q[ k+1 ] - Q[ k ];
/* the Ptmp and Qtmp values at this stage need to fit in int32 */
a32_QA1[ k ] = -Qtmp - Ptmp; /* QA+1 */
a32_QA1[ d-k-1 ] = Qtmp - Ptmp; /* QA+1 */
}
/* Limit the maximum absolute value of the prediction coefficients, so that they'll fit in int16 */
for( i = 0; i < 10; i++ ) {
/* Find maximum absolute value and its index */
maxabs = 0;
for( k = 0; k < d; k++ ) {
absval = silk_abs( a32_QA1[k] );
if( absval > maxabs ) {
maxabs = absval;
idx = k;
}
}
maxabs = silk_RSHIFT_ROUND( maxabs, QA + 1 - 12 ); /* QA+1 -> Q12 */
if( maxabs > silk_int16_MAX ) {
/* Reduce magnitude of prediction coefficients */
maxabs = silk_min( maxabs, 163838 ); /* ( silk_int32_MAX >> 14 ) + silk_int16_MAX = 163838 */
sc_Q16 = SILK_FIX_CONST( 0.999, 16 ) - silk_DIV32( silk_LSHIFT( maxabs - silk_int16_MAX, 14 ),
silk_RSHIFT32( silk_MUL( maxabs, idx + 1), 2 ) );
silk_bwexpander_32( a32_QA1, d, sc_Q16 );
} else {
break;
}
}
if( i == 10 ) {
/* Reached the last iteration, clip the coefficients */
for( k = 0; k < d; k++ ) {
a_Q12[ k ] = (opus_int16)silk_SAT16( silk_RSHIFT_ROUND( a32_QA1[ k ], QA + 1 - 12 ) ); /* QA+1 -> Q12 */
a32_QA1[ k ] = silk_LSHIFT( (opus_int32)a_Q12[ k ], QA + 1 - 12 );
}
} else {
for( k = 0; k < d; k++ ) {
a_Q12[ k ] = (opus_int16)silk_RSHIFT_ROUND( a32_QA1[ k ], QA + 1 - 12 ); /* QA+1 -> Q12 */
}
}
for( i = 0; i < MAX_LPC_STABILIZE_ITERATIONS; i++ ) {
if( silk_LPC_inverse_pred_gain( a_Q12, d ) < SILK_FIX_CONST( 1.0 / MAX_PREDICTION_POWER_GAIN, 30 ) ) {
/* Prediction coefficients are (too close to) unstable; apply bandwidth expansion */
/* on the unscaled coefficients, convert to Q12 and measure again */
silk_bwexpander_32( a32_QA1, d, 65536 - silk_LSHIFT( 2, i ) );
for( k = 0; k < d; k++ ) {
a_Q12[ k ] = (opus_int16)silk_RSHIFT_ROUND( a32_QA1[ k ], QA + 1 - 12 ); /* QA+1 -> Q12 */
}
} else {
break;
}
}
}