| // 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 |
| |