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https://github.com/Relintai/pandemonium_engine_minimal.git
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80 lines
4.3 KiB
C
80 lines
4.3 KiB
C
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/***********************************************************************
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Copyright (c) 2006-2011, Skype Limited. All rights reserved.
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions
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are met:
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- Redistributions of source code must retain the above copyright notice,
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this list of conditions and the following disclaimer.
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- Redistributions in binary form must reproduce the above copyright
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notice, this list of conditions and the following disclaimer in the
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documentation and/or other materials provided with the distribution.
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- Neither the name of Internet Society, IETF or IETF Trust, nor the
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names of specific contributors, may be used to endorse or promote
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products derived from this software without specific prior written
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permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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POSSIBILITY OF SUCH DAMAGE.
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***********************************************************************/
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#ifdef HAVE_CONFIG_H
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#include "config.h"
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#endif
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#include "main.h"
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/* Find least-squares prediction gain for one signal based on another and quantize it */
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opus_int32 silk_stereo_find_predictor( /* O Returns predictor in Q13 */
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opus_int32 *ratio_Q14, /* O Ratio of residual and mid energies */
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const opus_int16 x[], /* I Basis signal */
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const opus_int16 y[], /* I Target signal */
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opus_int32 mid_res_amp_Q0[], /* I/O Smoothed mid, residual norms */
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opus_int length, /* I Number of samples */
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opus_int smooth_coef_Q16 /* I Smoothing coefficient */
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)
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{
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opus_int scale, scale1, scale2;
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opus_int32 nrgx, nrgy, corr, pred_Q13, pred2_Q10;
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/* Find predictor */
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silk_sum_sqr_shift( &nrgx, &scale1, x, length );
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silk_sum_sqr_shift( &nrgy, &scale2, y, length );
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scale = silk_max_int( scale1, scale2 );
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scale = scale + ( scale & 1 ); /* make even */
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nrgy = silk_RSHIFT32( nrgy, scale - scale2 );
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nrgx = silk_RSHIFT32( nrgx, scale - scale1 );
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nrgx = silk_max_int( nrgx, 1 );
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corr = silk_inner_prod_aligned_scale( x, y, scale, length );
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pred_Q13 = silk_DIV32_varQ( corr, nrgx, 13 );
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pred_Q13 = silk_LIMIT( pred_Q13, -(1 << 14), 1 << 14 );
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pred2_Q10 = silk_SMULWB( pred_Q13, pred_Q13 );
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/* Faster update for signals with large prediction parameters */
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smooth_coef_Q16 = (opus_int)silk_max_int( smooth_coef_Q16, silk_abs( pred2_Q10 ) );
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/* Smoothed mid and residual norms */
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silk_assert( smooth_coef_Q16 < 32768 );
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scale = silk_RSHIFT( scale, 1 );
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mid_res_amp_Q0[ 0 ] = silk_SMLAWB( mid_res_amp_Q0[ 0 ], silk_LSHIFT( silk_SQRT_APPROX( nrgx ), scale ) - mid_res_amp_Q0[ 0 ],
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smooth_coef_Q16 );
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/* Residual energy = nrgy - 2 * pred * corr + pred^2 * nrgx */
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nrgy = silk_SUB_LSHIFT32( nrgy, silk_SMULWB( corr, pred_Q13 ), 3 + 1 );
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nrgy = silk_ADD_LSHIFT32( nrgy, silk_SMULWB( nrgx, pred2_Q10 ), 6 );
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mid_res_amp_Q0[ 1 ] = silk_SMLAWB( mid_res_amp_Q0[ 1 ], silk_LSHIFT( silk_SQRT_APPROX( nrgy ), scale ) - mid_res_amp_Q0[ 1 ],
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smooth_coef_Q16 );
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/* Ratio of smoothed residual and mid norms */
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*ratio_Q14 = silk_DIV32_varQ( mid_res_amp_Q0[ 1 ], silk_max( mid_res_amp_Q0[ 0 ], 1 ), 14 );
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*ratio_Q14 = silk_LIMIT( *ratio_Q14, 0, 32767 );
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return pred_Q13;
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}
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