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// 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.
#include "media/filters/wsola_internals.h"
#include <algorithm>
#include <cmath>
#include <cstring>
#include <limits>
#include <memory>
#include "base/check_op.h"
#include "base/numerics/math_constants.h"
#include "build/build_config.h"
#include "media/base/audio_bus.h"
#if defined(ARCH_CPU_X86_FAMILY)
#define USE_SIMD 1
#include <xmmintrin.h>
#elif defined(ARCH_CPU_ARM_FAMILY) && defined(USE_NEON)
#define USE_SIMD 1
#include <arm_neon.h>
#endif
namespace media {
namespace internal {
bool InInterval(int n, Interval q) {
return n >= q.first && n <= q.second;
}
float MultiChannelSimilarityMeasure(const float* dot_prod_a_b,
const float* energy_a,
const float* energy_b,
int channels) {
const float kEpsilon = 1e-12f;
float similarity_measure = 0.0f;
for (int n = 0; n < channels; ++n) {
similarity_measure +=
dot_prod_a_b[n] / std::sqrt(energy_a[n] * energy_b[n] + kEpsilon);
}
return similarity_measure;
}
void MultiChannelDotProduct(const AudioBus* a,
int frame_offset_a,
const AudioBus* b,
int frame_offset_b,
int num_frames,
float* dot_product) {
DCHECK_EQ(a->channels(), b->channels());
DCHECK_GE(frame_offset_a, 0);
DCHECK_GE(frame_offset_b, 0);
DCHECK_LE(frame_offset_a + num_frames, a->frames());
DCHECK_LE(frame_offset_b + num_frames, b->frames());
// SIMD optimized variants can provide a massive speedup to this operation.
#if defined(USE_SIMD)
const int rem = num_frames % 4;
const int last_index = num_frames - rem;
const int channels = a->channels();
for (int ch = 0; ch < channels; ++ch) {
const float* a_src = a->channel(ch) + frame_offset_a;
const float* b_src = b->channel(ch) + frame_offset_b;
#if defined(ARCH_CPU_X86_FAMILY)
// First sum all components.
__m128 m_sum = _mm_setzero_ps();
for (int s = 0; s < last_index; s += 4) {
m_sum = _mm_add_ps(
m_sum, _mm_mul_ps(_mm_loadu_ps(a_src + s), _mm_loadu_ps(b_src + s)));
}
// Reduce to a single float for this channel. Sadly, SSE1,2 doesn't have a
// horizontal sum function, so we have to condense manually.
m_sum = _mm_add_ps(_mm_movehl_ps(m_sum, m_sum), m_sum);
_mm_store_ss(dot_product + ch,
_mm_add_ss(m_sum, _mm_shuffle_ps(m_sum, m_sum, 1)));
#elif defined(ARCH_CPU_ARM_FAMILY)
// First sum all components.
float32x4_t m_sum = vmovq_n_f32(0);
for (int s = 0; s < last_index; s += 4)
m_sum = vmlaq_f32(m_sum, vld1q_f32(a_src + s), vld1q_f32(b_src + s));
// Reduce to a single float for this channel.
float32x2_t m_half = vadd_f32(vget_high_f32(m_sum), vget_low_f32(m_sum));
dot_product[ch] = vget_lane_f32(vpadd_f32(m_half, m_half), 0);
#endif
}
if (!rem)
return;
num_frames = rem;
frame_offset_a += last_index;
frame_offset_b += last_index;
#else
memset(dot_product, 0, sizeof(*dot_product) * a->channels());
#endif // defined(USE_SIMD)
// C version is required to handle remainder of frames (% 4 != 0)
for (int k = 0; k < a->channels(); ++k) {
const float* ch_a = a->channel(k) + frame_offset_a;
const float* ch_b = b->channel(k) + frame_offset_b;
for (int n = 0; n < num_frames; ++n)
dot_product[k] += *ch_a++ * *ch_b++;
}
}
void MultiChannelMovingBlockEnergies(const AudioBus* input,
int frames_per_block,
float* energy) {
int num_blocks = input->frames() - (frames_per_block - 1);
int channels = input->channels();
for (int k = 0; k < input->channels(); ++k) {
const float* input_channel = input->channel(k);
energy[k] = 0;
// First block of channel |k|.
for (int m = 0; m < frames_per_block; ++m) {
energy[k] += input_channel[m] * input_channel[m];
}
const float* slide_out = input_channel;
const float* slide_in = input_channel + frames_per_block;
for (int n = 1; n < num_blocks; ++n, ++slide_in, ++slide_out) {
energy[k + n * channels] = energy[k + (n - 1) * channels] - *slide_out *
*slide_out + *slide_in * *slide_in;
}
}
}
// Fit the curve f(x) = a * x^2 + b * x + c such that
// f(-1) = y[0]
// f(0) = y[1]
// f(1) = y[2]
// and return the maximum, assuming that y[0] <= y[1] >= y[2].
void QuadraticInterpolation(const float* y_values,
float* extremum,
float* extremum_value) {
float a = 0.5f * (y_values[2] + y_values[0]) - y_values[1];
float b = 0.5f * (y_values[2] - y_values[0]);
float c = y_values[1];
if (a == 0.f) {
// The coordinates are colinear (within floating-point error).
*extremum = 0;
*extremum_value = y_values[1];
} else {
*extremum = -b / (2.f * a);
*extremum_value = a * (*extremum) * (*extremum) + b * (*extremum) + c;
}
}
int DecimatedSearch(int decimation,
Interval exclude_interval,
const AudioBus* target_block,
const AudioBus* search_segment,
const float* energy_target_block,
const float* energy_candidate_blocks) {
int channels = search_segment->channels();
int block_size = target_block->frames();
int num_candidate_blocks = search_segment->frames() - (block_size - 1);
std::unique_ptr<float[]> dot_prod(new float[channels]);
float similarity[3]; // Three elements for cubic interpolation.
int n = 0;
MultiChannelDotProduct(target_block, 0, search_segment, n, block_size,
dot_prod.get());
similarity[0] = MultiChannelSimilarityMeasure(
dot_prod.get(), energy_target_block,
&energy_candidate_blocks[n * channels], channels);
// Set the starting point as optimal point.
float best_similarity = similarity[0];
int optimal_index = 0;
n += decimation;
if (n >= num_candidate_blocks) {
return 0;
}
MultiChannelDotProduct(target_block, 0, search_segment, n, block_size,
dot_prod.get());
similarity[1] = MultiChannelSimilarityMeasure(
dot_prod.get(), energy_target_block,
&energy_candidate_blocks[n * channels], channels);
n += decimation;
if (n >= num_candidate_blocks) {
// We cannot do any more sampling. Compare these two values and return the
// optimal index.
return similarity[1] > similarity[0] ? decimation : 0;
}
for (; n < num_candidate_blocks; n += decimation) {
MultiChannelDotProduct(target_block, 0, search_segment, n, block_size,
dot_prod.get());
similarity[2] = MultiChannelSimilarityMeasure(
dot_prod.get(), energy_target_block,
&energy_candidate_blocks[n * channels], channels);
if ((similarity[1] > similarity[0] && similarity[1] >= similarity[2]) ||
(similarity[1] >= similarity[0] && similarity[1] > similarity[2])) {
// A local maximum is found. Do a cubic interpolation for a better
// estimate of candidate maximum.
float normalized_candidate_index;
float candidate_similarity;
QuadraticInterpolation(similarity, &normalized_candidate_index,
&candidate_similarity);
int candidate_index = n - decimation + static_cast<int>(
normalized_candidate_index * decimation + 0.5f);
if (candidate_similarity > best_similarity &&
!InInterval(candidate_index, exclude_interval)) {
optimal_index = candidate_index;
best_similarity = candidate_similarity;
}
} else if (n + decimation >= num_candidate_blocks &&
similarity[2] > best_similarity &&
!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;
}
int FullSearch(int low_limit,
int high_limit,
Interval exclude_interval,
const AudioBus* target_block,
const AudioBus* search_block,
const float* energy_target_block,
const float* energy_candidate_blocks) {
int channels = search_block->channels();
int block_size = target_block->frames();
std::unique_ptr<float[]> dot_prod(new float[channels]);
float best_similarity = std::numeric_limits<float>::min();
int optimal_index = 0;
for (int n = low_limit; n <= high_limit; ++n) {
if (InInterval(n, exclude_interval)) {
continue;
}
MultiChannelDotProduct(target_block, 0, search_block, n, block_size,
dot_prod.get());
float similarity = MultiChannelSimilarityMeasure(
dot_prod.get(), energy_target_block,
&energy_candidate_blocks[n * channels], channels);
if (similarity > best_similarity) {
best_similarity = similarity;
optimal_index = n;
}
}
return optimal_index;
}
int OptimalIndex(const AudioBus* search_block,
const AudioBus* target_block,
Interval exclude_interval) {
int channels = search_block->channels();
DCHECK_EQ(channels, target_block->channels());
int target_size = target_block->frames();
int num_candidate_blocks = search_block->frames() - (target_size - 1);
// This is a compromise between complexity reduction and search accuracy. I
// don't have a proof that down sample of order 5 is optimal. One can compute
// a decimation factor that minimizes complexity given the size of
// |search_block| and |target_block|. However, my experiments show the rate of
// missing the optimal index is significant. This value is chosen
// heuristically based on experiments.
const int kSearchDecimation = 5;
std::unique_ptr<float[]> energy_target_block(new float[channels]);
std::unique_ptr<float[]> energy_candidate_blocks(
new float[channels * num_candidate_blocks]);
// Energy of all candid frames.
MultiChannelMovingBlockEnergies(search_block, target_size,
energy_candidate_blocks.get());
// Energy of target frame.
MultiChannelDotProduct(target_block, 0, target_block, 0,
target_size, energy_target_block.get());
int optimal_index = DecimatedSearch(kSearchDecimation,
exclude_interval, target_block,
search_block, energy_target_block.get(),
energy_candidate_blocks.get());
int lim_low = std::max(0, optimal_index - kSearchDecimation);
int lim_high = std::min(num_candidate_blocks - 1,
optimal_index + kSearchDecimation);
return FullSearch(lim_low, lim_high, exclude_interval, target_block,
search_block, energy_target_block.get(),
energy_candidate_blocks.get());
}
void GetPeriodicHanningWindow(int window_length, float* window) {
const float scale = 2.0f * base::kPiFloat / window_length;
for (int n = 0; n < window_length; ++n)
window[n] = 0.5f * (1.0f - std::cos(n * scale));
}
} // namespace internal
} // namespace media