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1 // Copyright 2013 The Chromium Authors. All rights reserved. | |
2 // Use of this source code is governed by a BSD-style license that can be | |
3 // found in the LICENSE file. | |
4 | |
5 // MSVC++ requires this to be set before any other includes to get M_PI. | |
6 #define _USE_MATH_DEFINES | |
7 | |
8 #include "media/filters/wsola_internals.h" | |
9 | |
10 #include <algorithm> | |
11 #include <cmath> | |
12 #include <limits> | |
13 | |
14 #include "base/logging.h" | |
15 #include "base/memory/scoped_ptr.h" | |
16 #include "media/base/audio_bus.h" | |
17 | |
18 namespace media { | |
19 | |
20 namespace internal { | |
21 | |
22 bool InInterval(int n, Interval q) { | |
23 return n >= q.first && n <= q.second; | |
24 } | |
25 | |
26 float MultiChannelSimilarityMeasure(const float* dot_prod_a_b, | |
27 const float* energy_a, | |
28 const float* energy_b, | |
29 int channels) { | |
30 const float kEpsilon = 1e-12; | |
31 float similarity_measure = 0; | |
32 for (int n = 0; n < channels; ++n) { | |
33 similarity_measure += dot_prod_a_b[n] / sqrt(energy_a[n] * energy_b[n] + | |
34 kEpsilon); | |
35 } | |
36 return similarity_measure; | |
37 } | |
38 | |
39 void MultiChannelDotProduct(const AudioBus* a, | |
40 int frame_offset_a, | |
41 const AudioBus* b, | |
42 int frame_offset_b, | |
43 int num_frames, | |
44 float* dot_product) { | |
45 DCHECK_EQ(a->channels(), b->channels()); | |
46 DCHECK_GE(frame_offset_a, 0); | |
47 DCHECK_GE(frame_offset_b, 0); | |
48 DCHECK_LE(frame_offset_a + num_frames, a->frames()); | |
49 DCHECK_LE(frame_offset_b + num_frames, b->frames()); | |
50 | |
51 memset(dot_product, 0, sizeof(*dot_product) * a->channels()); | |
52 for (int k = 0; k < a->channels(); ++k) { | |
53 const float* ch_a = a->channel(k) + frame_offset_a; | |
54 const float* ch_b = b->channel(k) + frame_offset_b; | |
55 for (int n = 0; n < num_frames; ++n) { | |
56 dot_product[k] += *ch_a++ * *ch_b++; | |
57 } | |
58 } | |
59 } | |
60 | |
61 void MultiChannelMovingBlockEnergies(const AudioBus* input, | |
62 int frames_per_block, | |
63 float* energy) { | |
64 int num_blocks = input->frames() - (frames_per_block - 1); | |
65 int channels = input->channels(); | |
66 | |
67 for (int k = 0; k < input->channels(); ++k) { | |
68 const float* input_channel = input->channel(k); | |
69 | |
70 energy[k] = 0; | |
71 | |
72 // First block of channel |k|. | |
73 for (int m = 0; m < frames_per_block; ++m) { | |
74 energy[k] += input_channel[m] * input_channel[m]; | |
75 } | |
76 | |
77 const float* slide_out = input_channel; | |
78 const float* slide_in = input_channel + frames_per_block; | |
79 for (int n = 1; n < num_blocks; ++n, ++slide_in, ++slide_out) { | |
80 energy[k + n * channels] = energy[k + (n - 1) * channels] - *slide_out * | |
81 *slide_out + *slide_in * *slide_in; | |
82 } | |
83 } | |
84 } | |
85 | |
86 // Fit the curve f(x) = a * x^2 + b * x + c such that | |
87 // f(-1) = |y[0]| | |
88 // f(0) = |y[1]| | |
89 // f(1) = |y[2]|. | |
90 void CubicInterpolation(const float* y_values, | |
91 float* extremum, | |
92 float* extremum_value) { | |
93 float a = 0.5f * (y_values[2] + y_values[0]) - y_values[1]; | |
94 float b = 0.5f * (y_values[2] - y_values[0]); | |
95 float c = y_values[1]; | |
96 | |
97 DCHECK_NE(a, 0); | |
98 *extremum = -b / (2.f * a); | |
99 *extremum_value = a * (*extremum) * (*extremum) + b * (*extremum) + c; | |
100 } | |
101 | |
102 int DecimatedSearch(int decimation, | |
103 Interval exclude_interval, | |
104 const AudioBus* target_block, | |
105 const AudioBus* search_segment, | |
106 const float* energy_target_block, | |
107 const float* energy_candidate_blocks) { | |
108 int channels = search_segment->channels(); | |
109 int block_size = target_block->frames(); | |
110 int num_candidate_blocks = search_segment->frames() - (block_size - 1); | |
111 scoped_ptr<float[]> dot_prod(new float[channels]); | |
112 float similarity[3]; // Three elements for cubic interpolation. | |
113 | |
114 int n = 0; | |
115 MultiChannelDotProduct(target_block, 0, search_segment, n, block_size, | |
116 dot_prod.get()); | |
117 similarity[0] = MultiChannelSimilarityMeasure( | |
118 dot_prod.get(), energy_target_block, | |
119 &energy_candidate_blocks[n * channels], channels); | |
120 | |
121 // Set the starting point as optimal point. | |
122 float best_similarity = similarity[0]; | |
123 int optimal_index = 0; | |
124 | |
125 n += decimation; | |
126 if (n >= num_candidate_blocks) { | |
127 return 0; | |
128 } | |
129 | |
130 MultiChannelDotProduct(target_block, 0, search_segment, n, block_size, | |
131 dot_prod.get()); | |
132 similarity[1] = MultiChannelSimilarityMeasure( | |
133 dot_prod.get(), energy_target_block, | |
134 &energy_candidate_blocks[n * channels], channels); | |
135 | |
136 n += decimation; | |
137 if (n >= num_candidate_blocks) { | |
138 // We cannot do any more sampling. Compare these two values and return the | |
139 // optimal index. | |
140 return similarity[1] > similarity[0] ? decimation : 0; | |
141 } | |
142 | |
143 for (; n < num_candidate_blocks; n += decimation) { | |
144 MultiChannelDotProduct(target_block, 0, search_segment, n, block_size, | |
145 dot_prod.get()); | |
146 | |
147 similarity[2] = MultiChannelSimilarityMeasure( | |
148 dot_prod.get(), energy_target_block, | |
149 &energy_candidate_blocks[n * channels], channels); | |
150 | |
151 if ((similarity[1] > similarity[0] && similarity[1] >= similarity[2]) || | |
152 (similarity[1] >= similarity[0] && similarity[1] > similarity[2])) { | |
153 // A local maximum is found. Do a cubic interpolation for a better | |
154 // estimate of candidate maximum. | |
155 float normalized_candidate_index; | |
156 float candidate_similarity; | |
157 CubicInterpolation(similarity, &normalized_candidate_index, | |
158 &candidate_similarity); | |
159 | |
160 int candidate_index = n - decimation + static_cast<int>( | |
161 normalized_candidate_index * decimation + 0.5f); | |
162 if (candidate_similarity > best_similarity && | |
163 !InInterval(candidate_index, exclude_interval)) { | |
164 optimal_index = candidate_index; | |
165 best_similarity = candidate_similarity; | |
166 } | |
167 } else if (n + decimation >= num_candidate_blocks && | |
168 similarity[2] > best_similarity && | |
169 !InInterval(n, exclude_interval)) { | |
170 // If this is the end-point and has a better similarity-measure than | |
171 // optimal, then we accept it as optimal point. | |
172 optimal_index = n; | |
173 best_similarity = similarity[2]; | |
174 } | |
175 memmove(similarity, &similarity[1], 2 * sizeof(*similarity)); | |
176 } | |
177 return optimal_index; | |
178 } | |
179 | |
180 int FullSearch(int low_limit, | |
181 int high_limit, | |
182 Interval exclude_interval, | |
183 const AudioBus* target_block, | |
184 const AudioBus* search_block, | |
185 const float* energy_target_block, | |
186 const float* energy_candidate_blocks) { | |
187 int channels = search_block->channels(); | |
188 int block_size = target_block->frames(); | |
189 scoped_ptr<float[]> dot_prod(new float[channels]); | |
190 | |
191 float best_similarity = std::numeric_limits<float>::min(); | |
192 int optimal_index = 0; | |
193 | |
194 for (int n = low_limit; n <= high_limit; ++n) { | |
195 if (InInterval(n, exclude_interval)) { | |
196 continue; | |
197 } | |
198 MultiChannelDotProduct(target_block, 0, search_block, n, block_size, | |
199 dot_prod.get()); | |
200 | |
201 float similarity = MultiChannelSimilarityMeasure( | |
202 dot_prod.get(), energy_target_block, | |
203 &energy_candidate_blocks[n * channels], channels); | |
204 | |
205 if (similarity > best_similarity) { | |
206 best_similarity = similarity; | |
207 optimal_index = n; | |
208 } | |
209 } | |
210 | |
211 return optimal_index; | |
212 } | |
213 | |
214 int OptimalIndex(const AudioBus* search_block, | |
215 const AudioBus* target_block, | |
216 Interval exclude_interval) { | |
217 int channels = search_block->channels(); | |
218 DCHECK_EQ(channels, target_block->channels()); | |
219 int target_size = target_block->frames(); | |
220 int num_candidate_blocks = search_block->frames() - (target_size - 1); | |
221 | |
222 // This is a compromise between complexity reduction and search accuracy. I | |
223 // don't have a proof that down sample of order 5 is optimal. One can compute | |
224 // a decimation factor that minimizes complexity given the size of | |
225 // |search_block| and |target_block|. However, my experiments show the rate of | |
226 // missing the optimal index is significant. This value is chosen | |
227 // heuristically based on experiments. | |
228 const int kSearchDecimation = 5; | |
229 | |
230 scoped_ptr<float[]> energy_target_block(new float[channels]); | |
231 scoped_ptr<float[]> energy_candidate_blocks( | |
232 new float[channels * num_candidate_blocks]); | |
233 | |
234 // Energy of all candid frames. | |
235 MultiChannelMovingBlockEnergies(search_block, target_size, | |
236 energy_candidate_blocks.get()); | |
237 | |
238 // Energy of target frame. | |
239 MultiChannelDotProduct(target_block, 0, target_block, 0, | |
240 target_size, energy_target_block.get()); | |
241 | |
242 int optimal_index = DecimatedSearch(kSearchDecimation, | |
243 exclude_interval, target_block, | |
244 search_block, energy_target_block.get(), | |
245 energy_candidate_blocks.get()); | |
246 | |
247 int lim_low = std::max(0, optimal_index - kSearchDecimation); | |
248 int lim_high = std::min(num_candidate_blocks - 1, | |
249 optimal_index + kSearchDecimation); | |
250 return FullSearch(lim_low, lim_high, exclude_interval, target_block, | |
251 search_block, energy_target_block.get(), | |
252 energy_candidate_blocks.get()); | |
253 } | |
254 | |
255 void GetSymmetricHanningWindow(int window_length, float* window) { | |
256 const float scale = 2.0f * M_PI / static_cast<float>(window_length); | |
DaleCurtis
2013/08/22 22:36:36
cast should be unnecessary.
turaj
2013/08/23 21:14:09
Done. I suppose M_PI is considered double, and the
| |
257 for (int n = 0; n < window_length; ++n) | |
258 window[n] = 0.5f * (1.0f - cosf(n * scale)); | |
259 } | |
260 | |
261 } // namespace internal | |
262 | |
263 } // namespace media | |
264 | |
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