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26 ***********************************************************************/
27
28 #ifdef HAVE_CONFIG_H
29 #include "config.h"
30 #endif
31
32 #include "SigProc_FLP.h"
33 #include "tuning_parameters.h"
34 #include "define.h"
35
36 #define MAX_FRAME_SIZE 384 /* subfr_length * nb_subfr = ( 0.005 * 16000 + 16 ) * 4 = 384*/
37
38 /* Compute reflection coefficients from input signal */
silk_burg_modified_FLP(silk_float A[],const silk_float x[],const silk_float minInvGain,const opus_int subfr_length,const opus_int nb_subfr,const opus_int D,int arch)39 silk_float silk_burg_modified_FLP( /* O returns residual energy */
40 silk_float A[], /* O prediction coefficients (length order) */
41 const silk_float x[], /* I input signal, length: nb_subfr*(D+L_sub) */
42 const silk_float minInvGain, /* I minimum inverse prediction gain */
43 const opus_int subfr_length, /* I input signal subframe length (incl. D preceding samples) */
44 const opus_int nb_subfr, /* I number of subframes stacked in x */
45 const opus_int D, /* I order */
46 int arch
47 )
48 {
49 opus_int k, n, s, reached_max_gain;
50 double C0, invGain, num, nrg_f, nrg_b, rc, Atmp, tmp1, tmp2;
51 const silk_float *x_ptr;
52 double C_first_row[ SILK_MAX_ORDER_LPC ], C_last_row[ SILK_MAX_ORDER_LPC ];
53 double CAf[ SILK_MAX_ORDER_LPC + 1 ], CAb[ SILK_MAX_ORDER_LPC + 1 ];
54 double Af[ SILK_MAX_ORDER_LPC ];
55
56 celt_assert( subfr_length * nb_subfr <= MAX_FRAME_SIZE );
57
58 /* Compute autocorrelations, added over subframes */
59 C0 = silk_energy_FLP( x, nb_subfr * subfr_length );
60 silk_memset( C_first_row, 0, SILK_MAX_ORDER_LPC * sizeof( double ) );
61 for( s = 0; s < nb_subfr; s++ ) {
62 x_ptr = x + s * subfr_length;
63 for( n = 1; n < D + 1; n++ ) {
64 C_first_row[ n - 1 ] += silk_inner_product_FLP( x_ptr, x_ptr + n, subfr_length - n, arch );
65 }
66 }
67 silk_memcpy( C_last_row, C_first_row, SILK_MAX_ORDER_LPC * sizeof( double ) );
68
69 /* Initialize */
70 CAb[ 0 ] = CAf[ 0 ] = C0 + FIND_LPC_COND_FAC * C0 + 1e-9f;
71 invGain = 1.0f;
72 reached_max_gain = 0;
73 for( n = 0; n < D; n++ ) {
74 /* Update first row of correlation matrix (without first element) */
75 /* Update last row of correlation matrix (without last element, stored in reversed order) */
76 /* Update C * Af */
77 /* Update C * flipud(Af) (stored in reversed order) */
78 for( s = 0; s < nb_subfr; s++ ) {
79 x_ptr = x + s * subfr_length;
80 tmp1 = x_ptr[ n ];
81 tmp2 = x_ptr[ subfr_length - n - 1 ];
82 for( k = 0; k < n; k++ ) {
83 C_first_row[ k ] -= x_ptr[ n ] * x_ptr[ n - k - 1 ];
84 C_last_row[ k ] -= x_ptr[ subfr_length - n - 1 ] * x_ptr[ subfr_length - n + k ];
85 Atmp = Af[ k ];
86 tmp1 += x_ptr[ n - k - 1 ] * Atmp;
87 tmp2 += x_ptr[ subfr_length - n + k ] * Atmp;
88 }
89 for( k = 0; k <= n; k++ ) {
90 CAf[ k ] -= tmp1 * x_ptr[ n - k ];
91 CAb[ k ] -= tmp2 * x_ptr[ subfr_length - n + k - 1 ];
92 }
93 }
94 tmp1 = C_first_row[ n ];
95 tmp2 = C_last_row[ n ];
96 for( k = 0; k < n; k++ ) {
97 Atmp = Af[ k ];
98 tmp1 += C_last_row[ n - k - 1 ] * Atmp;
99 tmp2 += C_first_row[ n - k - 1 ] * Atmp;
100 }
101 CAf[ n + 1 ] = tmp1;
102 CAb[ n + 1 ] = tmp2;
103
104 /* Calculate nominator and denominator for the next order reflection (parcor) coefficient */
105 num = CAb[ n + 1 ];
106 nrg_b = CAb[ 0 ];
107 nrg_f = CAf[ 0 ];
108 for( k = 0; k < n; k++ ) {
109 Atmp = Af[ k ];
110 num += CAb[ n - k ] * Atmp;
111 nrg_b += CAb[ k + 1 ] * Atmp;
112 nrg_f += CAf[ k + 1 ] * Atmp;
113 }
114 silk_assert( nrg_f > 0.0 );
115 silk_assert( nrg_b > 0.0 );
116
117 /* Calculate the next order reflection (parcor) coefficient */
118 rc = -2.0 * num / ( nrg_f + nrg_b );
119 silk_assert( rc > -1.0 && rc < 1.0 );
120
121 /* Update inverse prediction gain */
122 tmp1 = invGain * ( 1.0 - rc * rc );
123 if( tmp1 <= minInvGain ) {
124 /* Max prediction gain exceeded; set reflection coefficient such that max prediction gain is exactly hit */
125 rc = sqrt( 1.0 - minInvGain / invGain );
126 if( num > 0 ) {
127 /* Ensure adjusted reflection coefficients has the original sign */
128 rc = -rc;
129 }
130 invGain = minInvGain;
131 reached_max_gain = 1;
132 } else {
133 invGain = tmp1;
134 }
135
136 /* Update the AR coefficients */
137 for( k = 0; k < (n + 1) >> 1; k++ ) {
138 tmp1 = Af[ k ];
139 tmp2 = Af[ n - k - 1 ];
140 Af[ k ] = tmp1 + rc * tmp2;
141 Af[ n - k - 1 ] = tmp2 + rc * tmp1;
142 }
143 Af[ n ] = rc;
144
145 if( reached_max_gain ) {
146 /* Reached max prediction gain; set remaining coefficients to zero and exit loop */
147 for( k = n + 1; k < D; k++ ) {
148 Af[ k ] = 0.0;
149 }
150 break;
151 }
152
153 /* Update C * Af and C * Ab */
154 for( k = 0; k <= n + 1; k++ ) {
155 tmp1 = CAf[ k ];
156 CAf[ k ] += rc * CAb[ n - k + 1 ];
157 CAb[ n - k + 1 ] += rc * tmp1;
158 }
159 }
160
161 if( reached_max_gain ) {
162 /* Convert to silk_float */
163 for( k = 0; k < D; k++ ) {
164 A[ k ] = (silk_float)( -Af[ k ] );
165 }
166 /* Subtract energy of preceding samples from C0 */
167 for( s = 0; s < nb_subfr; s++ ) {
168 C0 -= silk_energy_FLP( x + s * subfr_length, D );
169 }
170 /* Approximate residual energy */
171 nrg_f = C0 * invGain;
172 } else {
173 /* Compute residual energy and store coefficients as silk_float */
174 nrg_f = CAf[ 0 ];
175 tmp1 = 1.0;
176 for( k = 0; k < D; k++ ) {
177 Atmp = Af[ k ];
178 nrg_f += CAf[ k + 1 ] * Atmp;
179 tmp1 += Atmp * Atmp;
180 A[ k ] = (silk_float)(-Atmp);
181 }
182 nrg_f -= FIND_LPC_COND_FAC * C0 * tmp1;
183 }
184
185 /* Return residual energy */
186 return (silk_float)nrg_f;
187 }
188