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Side by Side Diff: webrtc/modules/audio_processing/agc/pitch_based_vad.cc

Issue 1212543002: Pull the Voice Activity Detector out from the AGC (Closed) Base URL: https://chromium.googlesource.com/external/webrtc.git@master
Patch Set: Created 5 years, 5 months ago
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1 /*
2 * Copyright (c) 2012 The WebRTC project authors. All Rights Reserved.
3 *
4 * Use of this source code is governed by a BSD-style license
5 * that can be found in the LICENSE file in the root of the source
6 * tree. An additional intellectual property rights grant can be found
7 * in the file PATENTS. All contributing project authors may
8 * be found in the AUTHORS file in the root of the source tree.
9 */
10
11 #include "webrtc/modules/audio_processing/agc/pitch_based_vad.h"
12
13 #include <assert.h>
14 #include <math.h>
15 #include <string.h>
16
17 #include "webrtc/modules/audio_processing/agc/circular_buffer.h"
18 #include "webrtc/modules/audio_processing/agc/common.h"
19 #include "webrtc/modules/audio_processing/agc/noise_gmm_tables.h"
20 #include "webrtc/modules/audio_processing/agc/voice_gmm_tables.h"
21 #include "webrtc/modules/interface/module_common_types.h"
22
23 namespace webrtc {
24
25 static_assert(kNoiseGmmDim == kVoiceGmmDim,
26 "noise and voice gmm dimension not equal");
27
28 // These values should match MATLAB counterparts for unit-tests to pass.
29 static const int kPosteriorHistorySize = 500; // 5 sec of 10 ms frames.
30 static const double kInitialPriorProbability = 0.3;
31 static const int kTransientWidthThreshold = 7;
32 static const double kLowProbabilityThreshold = 0.2;
33
34 static double LimitProbability(double p) {
35 const double kLimHigh = 0.99;
36 const double kLimLow = 0.01;
37
38 if (p > kLimHigh)
39 p = kLimHigh;
40 else if (p < kLimLow)
41 p = kLimLow;
42 return p;
43 }
44
45 PitchBasedVad::PitchBasedVad()
46 : p_prior_(kInitialPriorProbability),
47 circular_buffer_(AgcCircularBuffer::Create(kPosteriorHistorySize)) {
48 // Setup noise GMM.
49 noise_gmm_.dimension = kNoiseGmmDim;
50 noise_gmm_.num_mixtures = kNoiseGmmNumMixtures;
51 noise_gmm_.weight = kNoiseGmmWeights;
52 noise_gmm_.mean = &kNoiseGmmMean[0][0];
53 noise_gmm_.covar_inverse = &kNoiseGmmCovarInverse[0][0][0];
54
55 // Setup voice GMM.
56 voice_gmm_.dimension = kVoiceGmmDim;
57 voice_gmm_.num_mixtures = kVoiceGmmNumMixtures;
58 voice_gmm_.weight = kVoiceGmmWeights;
59 voice_gmm_.mean = &kVoiceGmmMean[0][0];
60 voice_gmm_.covar_inverse = &kVoiceGmmCovarInverse[0][0][0];
61 }
62
63 PitchBasedVad::~PitchBasedVad() {}
64
65 int PitchBasedVad::VoicingProbability(const AudioFeatures& features,
66 double* p_combined) {
67 double p;
68 double gmm_features[3];
69 double pdf_features_given_voice;
70 double pdf_features_given_noise;
71 // These limits are the same in matlab implementation 'VoicingProbGMM().'
72 const double kLimLowLogPitchGain = -2.0;
73 const double kLimHighLogPitchGain = -0.9;
74 const double kLimLowSpectralPeak = 200;
75 const double kLimHighSpectralPeak = 2000;
76 const double kEps = 1e-12;
77 for (int n = 0; n < features.num_frames; n++) {
78 gmm_features[0] = features.log_pitch_gain[n];
79 gmm_features[1] = features.spectral_peak[n];
80 gmm_features[2] = features.pitch_lag_hz[n];
81
82 pdf_features_given_voice = EvaluateGmm(gmm_features, voice_gmm_);
83 pdf_features_given_noise = EvaluateGmm(gmm_features, noise_gmm_);
84
85 if (features.spectral_peak[n] < kLimLowSpectralPeak ||
86 features.spectral_peak[n] > kLimHighSpectralPeak ||
87 features.log_pitch_gain[n] < kLimLowLogPitchGain) {
88 pdf_features_given_voice = kEps * pdf_features_given_noise;
89 } else if (features.log_pitch_gain[n] > kLimHighLogPitchGain) {
90 pdf_features_given_noise = kEps * pdf_features_given_voice;
91 }
92
93 p = p_prior_ * pdf_features_given_voice / (pdf_features_given_voice *
94 p_prior_ + pdf_features_given_noise * (1 - p_prior_));
95
96 p = LimitProbability(p);
97
98 // Combine pitch-based probability with standalone probability, before
99 // updating prior probabilities.
100 double prod_active = p * p_combined[n];
101 double prod_inactive = (1 - p) * (1 - p_combined[n]);
102 p_combined[n] = prod_active / (prod_active + prod_inactive);
103
104 if (UpdatePrior(p_combined[n]) < 0)
105 return -1;
106 // Limit prior probability. With a zero prior probability the posterior
107 // probability is always zero.
108 p_prior_ = LimitProbability(p_prior_);
109 }
110 return 0;
111 }
112
113 int PitchBasedVad::UpdatePrior(double p) {
114 circular_buffer_->Insert(p);
115 if (circular_buffer_->RemoveTransient(kTransientWidthThreshold,
116 kLowProbabilityThreshold) < 0)
117 return -1;
118 p_prior_ = circular_buffer_->Mean();
119 return 0;
120 }
121
122 } // namespace webrtc
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