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qsidlsf.c
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/*
ITU-T G.729A Annex B ANSI-C Source Code
Version 1.3 Last modified: August 1997
Copyright (c) 1996, France Telecom, Rockwell International,
Universite de Sherbrooke.
All rights reserved.
*/
#include <stdio.h>
#include "typedef.h"
#include "basic_op.h"
#include "ld8a.h"
#include "tab_ld8a.h"
#include "sid.h"
#include "vad.h"
#include "dtx.h"
#include "tab_dtx.h"
/* local functions */
static void Qnt_e(int16_t *errlsf, /* (i) : error lsf vector */
int16_t *weight, /* (i) : weighting vector */
int16_t DIn, /* (i) : number of input candidates */
int16_t *qlsf, /* (o) : quantized error lsf vector */
int16_t *Pptr, /* (o) : predictor index */
int16_t DOut, /* (i) : number of quantized vectors */
int16_t *cluster, /* (o) : quantizer indices */
int16_t *MS /* (i) : size of the quantizers */
);
static void New_ML_search_1(int16_t *d_data, /* (i) : error vector */
int16_t J, /* (i) : number of input vectors */
int16_t *new_d_data,/* (o) : output vector */
int16_t K, /* (i) : number of candidates */
int16_t *best_indx, /* (o) : best indices */
int16_t *ptr_back, /* (o) : pointer for backtracking */
int16_t *PtrTab, /* (i) : quantizer table */
int16_t MQ /* (i) : size of quantizer */
);
static void New_ML_search_2(int16_t *d_data, /* (i) : error vector */
int16_t *weight, /* (i) : weighting vector */
int16_t J, /* (i) : number of input vectors */
int16_t *new_d_data,/* (o) : output vector */
int16_t K, /* (i) : number of candidates */
int16_t *best_indx, /* (o) : best indices */
int16_t *ptr_prd, /* (i) : pointer for backtracking */
int16_t *ptr_back, /* (o) : pointer for backtracking */
int16_t PtrTab[2][16],/* (i) : quantizer table */
int16_t MQ /* (i) : size of quantizer */
);
/*-----------------------------------------------------------------*
* Functions lsfq_noise *
* ~~~~~~~~~~ *
* Input: *
* lsp[] : unquantized lsp vector *
* freq_prev[][] : memory of the lsf predictor *
* *
* Output: *
* *
* lspq[] : quantized lsp vector *
* ana[] : indices *
* *
*-----------------------------------------------------------------*/
void lsfq_noise(int16_t *lsp,
int16_t *lspq,
int16_t freq_prev[MA_NP][M],
int16_t *ana
)
{
int16_t i, lsf[M], lsfq[M], weight[M], tmpbuf[M];
int16_t MS[MODE]={32, 16}, Clust[MODE], mode, errlsf[M*MODE];
/* convert lsp to lsf */
Lsp_lsf2(lsp, lsf, M);
/* spacing to ~100Hz */
if (lsf[0] < L_LIMIT)
lsf[0] = L_LIMIT;
for (i=0 ; i < M-1 ; i++)
if (sub(lsf[i+1], lsf[i]) < 2*GAP3)
lsf[i+1] = add(lsf[i], 2*GAP3);
if (lsf[M-1] > M_LIMIT)
lsf[M-1] = M_LIMIT;
if (lsf[M-1] < lsf[M-2])
lsf[M-2] = sub(lsf[M-1], GAP3);
/* get the lsf weighting */
Get_wegt(lsf, weight);
/**********************/
/* quantize the lsf's */
/**********************/
/* get the prediction error vector */
for (mode=0; mode<MODE; mode++)
Lsp_prev_extract(lsf, errlsf+mode*M, noise_fg[mode], freq_prev,
noise_fg_sum_inv[mode]);
/* quantize the lsf and get the corresponding indices */
Qnt_e(errlsf, weight, MODE, tmpbuf, &mode, 1, Clust, MS);
ana[0] = mode;
ana[1] = Clust[0];
ana[2] = Clust[1];
/* guarantee minimum distance of 0.0012 (~10 in Q13) between tmpbuf[j]
and tmpbuf[j+1] */
Lsp_expand_1_2(tmpbuf, 10);
/* compute the quantized lsf vector */
Lsp_prev_compose(tmpbuf, lsfq, noise_fg[mode], freq_prev,
noise_fg_sum[mode]);
/* update the prediction memory */
Lsp_prev_update(tmpbuf, freq_prev);
/* lsf stability check */
Lsp_stability(lsfq);
/* convert lsf to lsp */
Lsf_lsp2(lsfq, lspq, M);
}
static void Qnt_e(int16_t *errlsf, /* (i) : error lsf vector */
int16_t *weight, /* (i) : weighting vector */
int16_t DIn, /* (i) : number of input candidates */
int16_t *qlsf, /* (o) : quantized error lsf vector */
int16_t *Pptr, /* (o) : predictor index */
int16_t DOut, /* (i) : number of quantized vectors */
int16_t *cluster, /* (o) : quantizer indices */
int16_t *MS /* (i) : size of the quantizers */
)
{
int16_t d_data[2][R_LSFQ*M], best_indx[2][R_LSFQ];
int16_t ptr_back[2][R_LSFQ], ptr, i;
New_ML_search_1(errlsf, DIn, d_data[0], 4, best_indx[0], ptr_back[0],
PtrTab_1, MS[0]);
New_ML_search_2(d_data[0], weight, 4, d_data[1], DOut, best_indx[1],
ptr_back[0], ptr_back[1], PtrTab_2, MS[1]);
/* backward path for the indices */
cluster[1] = best_indx[1][0];
ptr = ptr_back[1][0];
cluster[0] = best_indx[0][ptr];
/* this is the pointer to the best predictor */
*Pptr = ptr_back[0][ptr];
/* generating the quantized vector */
Copy(lspcb1[PtrTab_1[cluster[0]]], qlsf, M);
for (i=0; i<M/2; i++)
qlsf[i] = add(qlsf[i], lspcb2[PtrTab_2[0][cluster[1]]][i]);
for (i=M/2; i<M; i++)
qlsf[i] = add(qlsf[i], lspcb2[PtrTab_2[1][cluster[1]]][i]);
}
static void New_ML_search_1(int16_t *d_data, /* (i) : error vector */
int16_t J, /* (i) : number of input vectors */
int16_t *new_d_data,/* (o) : output vector */
int16_t K, /* (i) : number of candidates */
int16_t *best_indx, /* (o) : best indices */
int16_t *ptr_back, /* (o) : pointer for backtracking */
int16_t *PtrTab, /* (i) : quantizer table */
int16_t MQ /* (i) : size of quantizer */
)
{
int16_t tmp, m, l, p, q, sum[R_LSFQ*R_LSFQ];
int16_t min[R_LSFQ], min_indx_p[R_LSFQ], min_indx_m[R_LSFQ];
int32_t acc0;
for (q=0; q<K; q++)
min[q] = MAX_16;
/* compute the errors */
for (p=0; p<J; p++)
for (m=0; m<MQ; m++){
acc0 = 0;
for (l=0; l<M; l++){
tmp = sub(d_data[p*M+l], lspcb1[PtrTab[m]][l]);
acc0 = L_mac(acc0, tmp, tmp);
}
sum[p*MQ+m] = extract_h(acc0);
sum[p*MQ+m] = mult(sum[p*MQ+m], Mp[p]);
}
/* select the candidates */
for (q=0; q<K; q++){
for (p=0; p<J; p++)
for (m=0; m<MQ; m++)
if (sub(sum[p*MQ+m], min[q]) < 0){
min[q] = sum[p*MQ+m];
min_indx_p[q] = p;
min_indx_m[q] = m;
}
sum[min_indx_p[q]*MQ+min_indx_m[q]] = MAX_16;
}
/* compute the candidates */
for (q=0; q<K; q++){
for (l=0; l<M; l++)
new_d_data[q*M+l] = sub(d_data[min_indx_p[q]*M+l],
lspcb1[PtrTab[min_indx_m[q]]][l]);
ptr_back[q] = min_indx_p[q];
best_indx[q] = min_indx_m[q];
}
}
static void New_ML_search_2(int16_t *d_data, /* (i) : error vector */
int16_t *weight, /* (i) : weighting vector */
int16_t J, /* (i) : number of input vectors */
int16_t *new_d_data,/* (o) : output vector */
int16_t K, /* (i) : number of candidates */
int16_t *best_indx, /* (o) : best indices */
int16_t *ptr_prd, /* (i) : pointer for backtracking */
int16_t *ptr_back, /* (o) : pointer for backtracking */
int16_t PtrTab[2][16],/* (i) : quantizer table */
int16_t MQ /* (i) : size of quantizer */
)
{
int16_t m, l, p, q, sum[R_LSFQ*R_LSFQ];
int16_t min[R_LSFQ], min_indx_p[R_LSFQ], min_indx_m[R_LSFQ];
int16_t tmp1, tmp2;
int32_t acc0;
for (q=0; q<K; q++)
min[q] = MAX_16;
/* compute the errors */
for (p=0; p<J; p++)
for (m=0; m<MQ; m++){
acc0 = 0;
for (l=0; l<M/2; l++){
tmp1 = extract_h(L_shl(L_mult(noise_fg_sum[ptr_prd[p]][l],
noise_fg_sum[ptr_prd[p]][l]), 2));
tmp1 = mult(tmp1, weight[l]);
tmp2 = sub(d_data[p*M+l], lspcb2[PtrTab[0][m]][l]);
tmp1 = extract_h(L_shl(L_mult(tmp1, tmp2), 3));
acc0 = L_mac(acc0, tmp1, tmp2);
}
for (l=M/2; l<M; l++){
tmp1 = extract_h(L_shl(L_mult(noise_fg_sum[ptr_prd[p]][l],
noise_fg_sum[ptr_prd[p]][l]), 2));
tmp1 = mult(tmp1, weight[l]);
tmp2 = sub(d_data[p*M+l], lspcb2[PtrTab[1][m]][l]);
tmp1 = extract_h(L_shl(L_mult(tmp1, tmp2), 3));
acc0 = L_mac(acc0, tmp1, tmp2);
}
sum[p*MQ+m] = extract_h(acc0);
}
/* select the candidates */
for (q=0; q<K; q++){
for (p=0; p<J; p++)
for (m=0; m<MQ; m++)
if (sub(sum[p*MQ+m], min[q]) < 0){
min[q] = sum[p*MQ+m];
min_indx_p[q] = p;
min_indx_m[q] = m;
}
sum[min_indx_p[q]*MQ+min_indx_m[q]] = MAX_16;
}
/* compute the candidates */
for (q=0; q<K; q++){
for (l=0; l<M/2; l++)
new_d_data[q*M+l] = sub(d_data[min_indx_p[q]*M+l],
lspcb2[PtrTab[0][min_indx_m[q]]][l]);
for (l=M/2; l<M; l++)
new_d_data[q*M+l] = sub(d_data[min_indx_p[q]*M+l],
lspcb2[PtrTab[1][min_indx_m[q]]][l]);
ptr_back[q] = min_indx_p[q];
best_indx[q] = min_indx_m[q];
}
}