| Index: media/filters/wsola_internals.cc
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| diff --git a/media/filters/wsola_internals.cc b/media/filters/wsola_internals.cc
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| new file mode 100644
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| index 0000000000000000000000000000000000000000..45cdd8ffad5de606c74a9165eb2dfd0d720b8816
|
| --- /dev/null
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| +++ b/media/filters/wsola_internals.cc
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| @@ -0,0 +1,264 @@
|
| +// Copyright 2013 The Chromium Authors. All rights reserved.
|
| +// Use of this source code is governed by a BSD-style license that can be
|
| +// found in the LICENSE file.
|
| +
|
| +// MSVC++ requires this to be set before any other includes to get M_PI.
|
| +#define _USE_MATH_DEFINES
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| +
|
| +#include "media/filters/wsola_internals.h"
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| +
|
| +#include <algorithm>
|
| +#include <cmath>
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| +#include <limits>
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| +
|
| +#include "base/logging.h"
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| +#include "base/memory/scoped_ptr.h"
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| +#include "media/base/audio_bus.h"
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| +
|
| +namespace media {
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| +
|
| +namespace internal {
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| +
|
| +bool InInterval(int n, Interval q) {
|
| + return n >= q.first && n <= q.second;
|
| +}
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| +
|
| +float MultiChannelSimilarityMeasure(const float* dot_prod_a_b,
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| + const float* energy_a,
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| + const float* energy_b,
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| + int channels) {
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| + const float kEpsilon = 1e-12f;
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| + float similarity_measure = 0.0f;
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| + for (int n = 0; n < channels; ++n) {
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| + similarity_measure += dot_prod_a_b[n] / sqrt(energy_a[n] * energy_b[n] +
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| + kEpsilon);
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| + }
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| + return similarity_measure;
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| +}
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| +
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| +void MultiChannelDotProduct(const AudioBus* a,
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| + int frame_offset_a,
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| + const AudioBus* b,
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| + int frame_offset_b,
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| + int num_frames,
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| + float* dot_product) {
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| + DCHECK_EQ(a->channels(), b->channels());
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| + DCHECK_GE(frame_offset_a, 0);
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| + DCHECK_GE(frame_offset_b, 0);
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| + DCHECK_LE(frame_offset_a + num_frames, a->frames());
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| + DCHECK_LE(frame_offset_b + num_frames, b->frames());
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| +
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| + memset(dot_product, 0, sizeof(*dot_product) * a->channels());
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| + for (int k = 0; k < a->channels(); ++k) {
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| + const float* ch_a = a->channel(k) + frame_offset_a;
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| + const float* ch_b = b->channel(k) + frame_offset_b;
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| + for (int n = 0; n < num_frames; ++n) {
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| + dot_product[k] += *ch_a++ * *ch_b++;
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| + }
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| + }
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| +}
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| +
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| +void MultiChannelMovingBlockEnergies(const AudioBus* input,
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| + int frames_per_block,
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| + float* energy) {
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| + int num_blocks = input->frames() - (frames_per_block - 1);
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| + int channels = input->channels();
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| +
|
| + for (int k = 0; k < input->channels(); ++k) {
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| + const float* input_channel = input->channel(k);
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| +
|
| + energy[k] = 0;
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| +
|
| + // First block of channel |k|.
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| + for (int m = 0; m < frames_per_block; ++m) {
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| + energy[k] += input_channel[m] * input_channel[m];
|
| + }
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| +
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| + const float* slide_out = input_channel;
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| + const float* slide_in = input_channel + frames_per_block;
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| + for (int n = 1; n < num_blocks; ++n, ++slide_in, ++slide_out) {
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| + energy[k + n * channels] = energy[k + (n - 1) * channels] - *slide_out *
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| + *slide_out + *slide_in * *slide_in;
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| + }
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| + }
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| +}
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| +
|
| +// Fit the curve f(x) = a * x^2 + b * x + c such that
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| +// f(-1) = |y[0]|
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| +// f(0) = |y[1]|
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| +// f(1) = |y[2]|.
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| +void CubicInterpolation(const float* y_values,
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| + float* extremum,
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| + float* extremum_value) {
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| + float a = 0.5f * (y_values[2] + y_values[0]) - y_values[1];
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| + float b = 0.5f * (y_values[2] - y_values[0]);
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| + float c = y_values[1];
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| +
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| + DCHECK_NE(a, 0);
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| + *extremum = -b / (2.f * a);
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| + *extremum_value = a * (*extremum) * (*extremum) + b * (*extremum) + c;
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| +}
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| +
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| +int DecimatedSearch(int decimation,
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| + Interval exclude_interval,
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| + const AudioBus* target_block,
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| + const AudioBus* search_segment,
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| + const float* energy_target_block,
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| + const float* energy_candidate_blocks) {
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| + int channels = search_segment->channels();
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| + int block_size = target_block->frames();
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| + int num_candidate_blocks = search_segment->frames() - (block_size - 1);
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| + scoped_ptr<float[]> dot_prod(new float[channels]);
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| + float similarity[3]; // Three elements for cubic interpolation.
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| +
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| + int n = 0;
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| + MultiChannelDotProduct(target_block, 0, search_segment, n, block_size,
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| + dot_prod.get());
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| + similarity[0] = MultiChannelSimilarityMeasure(
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| + dot_prod.get(), energy_target_block,
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| + &energy_candidate_blocks[n * channels], channels);
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| +
|
| + // Set the starting point as optimal point.
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| + float best_similarity = similarity[0];
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| + int optimal_index = 0;
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| +
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| + n += decimation;
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| + if (n >= num_candidate_blocks) {
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| + return 0;
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| + }
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| +
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| + MultiChannelDotProduct(target_block, 0, search_segment, n, block_size,
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| + dot_prod.get());
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| + similarity[1] = MultiChannelSimilarityMeasure(
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| + dot_prod.get(), energy_target_block,
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| + &energy_candidate_blocks[n * channels], channels);
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| +
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| + n += decimation;
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| + if (n >= num_candidate_blocks) {
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| + // We cannot do any more sampling. Compare these two values and return the
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| + // optimal index.
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| + return similarity[1] > similarity[0] ? decimation : 0;
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| + }
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| +
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| + for (; n < num_candidate_blocks; n += decimation) {
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| + MultiChannelDotProduct(target_block, 0, search_segment, n, block_size,
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| + dot_prod.get());
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| +
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| + similarity[2] = MultiChannelSimilarityMeasure(
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| + dot_prod.get(), energy_target_block,
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| + &energy_candidate_blocks[n * channels], channels);
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| +
|
| + if ((similarity[1] > similarity[0] && similarity[1] >= similarity[2]) ||
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| + (similarity[1] >= similarity[0] && similarity[1] > similarity[2])) {
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| + // A local maximum is found. Do a cubic interpolation for a better
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| + // estimate of candidate maximum.
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| + float normalized_candidate_index;
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| + float candidate_similarity;
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| + CubicInterpolation(similarity, &normalized_candidate_index,
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| + &candidate_similarity);
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| +
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| + int candidate_index = n - decimation + static_cast<int>(
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| + normalized_candidate_index * decimation + 0.5f);
|
| + if (candidate_similarity > best_similarity &&
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| + !InInterval(candidate_index, exclude_interval)) {
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| + optimal_index = candidate_index;
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| + best_similarity = candidate_similarity;
|
| + }
|
| + } else if (n + decimation >= num_candidate_blocks &&
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| + similarity[2] > best_similarity &&
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| + !InInterval(n, exclude_interval)) {
|
| + // If this is the end-point and has a better similarity-measure than
|
| + // optimal, then we accept it as optimal point.
|
| + optimal_index = n;
|
| + best_similarity = similarity[2];
|
| + }
|
| + memmove(similarity, &similarity[1], 2 * sizeof(*similarity));
|
| + }
|
| + return optimal_index;
|
| +}
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| +
|
| +int FullSearch(int low_limit,
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| + int high_limit,
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| + Interval exclude_interval,
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| + const AudioBus* target_block,
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| + const AudioBus* search_block,
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| + const float* energy_target_block,
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| + const float* energy_candidate_blocks) {
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| + int channels = search_block->channels();
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| + int block_size = target_block->frames();
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| + scoped_ptr<float[]> dot_prod(new float[channels]);
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| +
|
| + float best_similarity = std::numeric_limits<float>::min();
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| + int optimal_index = 0;
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| +
|
| + for (int n = low_limit; n <= high_limit; ++n) {
|
| + if (InInterval(n, exclude_interval)) {
|
| + continue;
|
| + }
|
| + MultiChannelDotProduct(target_block, 0, search_block, n, block_size,
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| + dot_prod.get());
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| +
|
| + float similarity = MultiChannelSimilarityMeasure(
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| + dot_prod.get(), energy_target_block,
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| + &energy_candidate_blocks[n * channels], channels);
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| +
|
| + if (similarity > best_similarity) {
|
| + best_similarity = similarity;
|
| + optimal_index = n;
|
| + }
|
| + }
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| +
|
| + return optimal_index;
|
| +}
|
| +
|
| +int OptimalIndex(const AudioBus* search_block,
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| + const AudioBus* target_block,
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| + Interval exclude_interval) {
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| + int channels = search_block->channels();
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| + DCHECK_EQ(channels, target_block->channels());
|
| + int target_size = target_block->frames();
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| + int num_candidate_blocks = search_block->frames() - (target_size - 1);
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| +
|
| + // This is a compromise between complexity reduction and search accuracy. I
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| + // don't have a proof that down sample of order 5 is optimal. One can compute
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| + // a decimation factor that minimizes complexity given the size of
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| + // |search_block| and |target_block|. However, my experiments show the rate of
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| + // missing the optimal index is significant. This value is chosen
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| + // heuristically based on experiments.
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| + const int kSearchDecimation = 5;
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| +
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| + scoped_ptr<float[]> energy_target_block(new float[channels]);
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| + scoped_ptr<float[]> energy_candidate_blocks(
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| + new float[channels * num_candidate_blocks]);
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| +
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| + // Energy of all candid frames.
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| + MultiChannelMovingBlockEnergies(search_block, target_size,
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| + energy_candidate_blocks.get());
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| +
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| + // Energy of target frame.
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| + MultiChannelDotProduct(target_block, 0, target_block, 0,
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| + target_size, energy_target_block.get());
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| +
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| + int optimal_index = DecimatedSearch(kSearchDecimation,
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| + exclude_interval, target_block,
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| + search_block, energy_target_block.get(),
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| + energy_candidate_blocks.get());
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| +
|
| + int lim_low = std::max(0, optimal_index - kSearchDecimation);
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| + int lim_high = std::min(num_candidate_blocks - 1,
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| + optimal_index + kSearchDecimation);
|
| + return FullSearch(lim_low, lim_high, exclude_interval, target_block,
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| + search_block, energy_target_block.get(),
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| + energy_candidate_blocks.get());
|
| +}
|
| +
|
| +void GetSymmetricHanningWindow(int window_length, float* window) {
|
| + const float scale = 2.0f * M_PI / window_length;
|
| + for (int n = 0; n < window_length; ++n)
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| + window[n] = 0.5f * (1.0f - cosf(n * scale));
|
| +}
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| +
|
| +} // namespace internal
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| +
|
| +} // namespace media
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| +
|
|
|