webrtc/modules/audio_processing/vad/pitch_based_vad.cc - Issue 1208793002: Revert "Pull the Voice Activity Detector out from the AGC"

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Issue 1208793002: Revert "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/vad/pitch_based_vad.h"

12

13 #include <assert.h>

14 #include <math.h>

15 #include <string.h>

16

17 #include "webrtc/modules/audio_processing/vad/vad_circular_buffer.h"

18 #include "webrtc/modules/audio_processing/vad/common.h"

19 #include "webrtc/modules/audio_processing/vad/noise_gmm_tables.h"

20 #include "webrtc/modules/audio_processing/vad/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_(VadCircularBuffer::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

66 int PitchBasedVad::VoicingProbability(const AudioFeatures& features,

67 double* p_combined) {

68 double p;

69 double gmm_features[3];

70 double pdf_features_given_voice;

71 double pdf_features_given_noise;

72 // These limits are the same in matlab implementation 'VoicingProbGMM().'

73 const double kLimLowLogPitchGain = -2.0;

74 const double kLimHighLogPitchGain = -0.9;

75 const double kLimLowSpectralPeak = 200;

76 const double kLimHighSpectralPeak = 2000;

77 const double kEps = 1e-12;

78 for (int n = 0; n < features.num_frames; n++) {

79 gmm_features[0] = features.log_pitch_gain[n];

80 gmm_features[1] = features.spectral_peak[n];

81 gmm_features[2] = features.pitch_lag_hz[n];

82

83 pdf_features_given_voice = EvaluateGmm(gmm_features, voice_gmm_);

84 pdf_features_given_noise = EvaluateGmm(gmm_features, noise_gmm_);

85

86 if (features.spectral_peak[n] < kLimLowSpectralPeak \|\|

87 features.spectral_peak[n] > kLimHighSpectralPeak \|\|

88 features.log_pitch_gain[n] < kLimLowLogPitchGain) {

89 pdf_features_given_voice = kEps * pdf_features_given_noise;

90 } else if (features.log_pitch_gain[n] > kLimHighLogPitchGain) {

91 pdf_features_given_noise = kEps * pdf_features_given_voice;

92 }

93

94 p = p_prior_ * pdf_features_given_voice /

95 (pdf_features_given_voice * p_prior_ +

96 pdf_features_given_noise * (1 - p_prior_));

97

98 p = LimitProbability(p);

99

100 // Combine pitch-based probability with standalone probability, before

101 // updating prior probabilities.

102 double prod_active = p * p_combined[n];

103 double prod_inactive = (1 - p) * (1 - p_combined[n]);

104 p_combined[n] = prod_active / (prod_active + prod_inactive);

105

106 if (UpdatePrior(p_combined[n]) < 0)

107 return -1;

108 // Limit prior probability. With a zero prior probability the posterior

109 // probability is always zero.

110 p_prior_ = LimitProbability(p_prior_);

111 }

112 return 0;

113 }

114

115 int PitchBasedVad::UpdatePrior(double p) {

116 circular_buffer_->Insert(p);

117 if (circular_buffer_->RemoveTransient(kTransientWidthThreshold,

118 kLowProbabilityThreshold) < 0)

119 return -1;

120 p_prior_ = circular_buffer_->Mean();

121 return 0;

122 }

123

124 } // namespace webrtc

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