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 "modules/audio_processing/vad/pitch_based_vad.h" 12 13 #include <string.h> 14 15 #include "modules/audio_processing/vad/common.h" 16 #include "modules/audio_processing/vad/noise_gmm_tables.h" 17 #include "modules/audio_processing/vad/vad_circular_buffer.h" 18 #include "modules/audio_processing/vad/voice_gmm_tables.h" 19 20 namespace webrtc { 21 22 static_assert(kNoiseGmmDim == kVoiceGmmDim, 23 "noise and voice gmm dimension not equal"); 24 25 // These values should match MATLAB counterparts for unit-tests to pass. 26 static const int kPosteriorHistorySize = 500; // 5 sec of 10 ms frames. 27 static const double kInitialPriorProbability = 0.3; 28 static const int kTransientWidthThreshold = 7; 29 static const double kLowProbabilityThreshold = 0.2; 30 LimitProbability(double p)31static double LimitProbability(double p) { 32 const double kLimHigh = 0.99; 33 const double kLimLow = 0.01; 34 35 if (p > kLimHigh) 36 p = kLimHigh; 37 else if (p < kLimLow) 38 p = kLimLow; 39 return p; 40 } 41 PitchBasedVad()42PitchBasedVad::PitchBasedVad() 43 : p_prior_(kInitialPriorProbability), 44 circular_buffer_(VadCircularBuffer::Create(kPosteriorHistorySize)) { 45 // Setup noise GMM. 46 noise_gmm_.dimension = kNoiseGmmDim; 47 noise_gmm_.num_mixtures = kNoiseGmmNumMixtures; 48 noise_gmm_.weight = kNoiseGmmWeights; 49 noise_gmm_.mean = &kNoiseGmmMean[0][0]; 50 noise_gmm_.covar_inverse = &kNoiseGmmCovarInverse[0][0][0]; 51 52 // Setup voice GMM. 53 voice_gmm_.dimension = kVoiceGmmDim; 54 voice_gmm_.num_mixtures = kVoiceGmmNumMixtures; 55 voice_gmm_.weight = kVoiceGmmWeights; 56 voice_gmm_.mean = &kVoiceGmmMean[0][0]; 57 voice_gmm_.covar_inverse = &kVoiceGmmCovarInverse[0][0][0]; 58 } 59 ~PitchBasedVad()60PitchBasedVad::~PitchBasedVad() {} 61 VoicingProbability(const AudioFeatures & features,double * p_combined)62int PitchBasedVad::VoicingProbability(const AudioFeatures& features, 63 double* p_combined) { 64 double p; 65 double gmm_features[3]; 66 double pdf_features_given_voice; 67 double pdf_features_given_noise; 68 // These limits are the same in matlab implementation 'VoicingProbGMM().' 69 const double kLimLowLogPitchGain = -2.0; 70 const double kLimHighLogPitchGain = -0.9; 71 const double kLimLowSpectralPeak = 200; 72 const double kLimHighSpectralPeak = 2000; 73 const double kEps = 1e-12; 74 for (size_t n = 0; n < features.num_frames; n++) { 75 gmm_features[0] = features.log_pitch_gain[n]; 76 gmm_features[1] = features.spectral_peak[n]; 77 gmm_features[2] = features.pitch_lag_hz[n]; 78 79 pdf_features_given_voice = EvaluateGmm(gmm_features, voice_gmm_); 80 pdf_features_given_noise = EvaluateGmm(gmm_features, noise_gmm_); 81 82 if (features.spectral_peak[n] < kLimLowSpectralPeak || 83 features.spectral_peak[n] > kLimHighSpectralPeak || 84 features.log_pitch_gain[n] < kLimLowLogPitchGain) { 85 pdf_features_given_voice = kEps * pdf_features_given_noise; 86 } else if (features.log_pitch_gain[n] > kLimHighLogPitchGain) { 87 pdf_features_given_noise = kEps * pdf_features_given_voice; 88 } 89 90 p = p_prior_ * pdf_features_given_voice / 91 (pdf_features_given_voice * p_prior_ + 92 pdf_features_given_noise * (1 - p_prior_)); 93 94 p = LimitProbability(p); 95 96 // Combine pitch-based probability with standalone probability, before 97 // updating prior probabilities. 98 double prod_active = p * p_combined[n]; 99 double prod_inactive = (1 - p) * (1 - p_combined[n]); 100 p_combined[n] = prod_active / (prod_active + prod_inactive); 101 102 if (UpdatePrior(p_combined[n]) < 0) 103 return -1; 104 // Limit prior probability. With a zero prior probability the posterior 105 // probability is always zero. 106 p_prior_ = LimitProbability(p_prior_); 107 } 108 return 0; 109 } 110 UpdatePrior(double p)111int PitchBasedVad::UpdatePrior(double p) { 112 circular_buffer_->Insert(p); 113 if (circular_buffer_->RemoveTransient(kTransientWidthThreshold, 114 kLowProbabilityThreshold) < 0) 115 return -1; 116 p_prior_ = circular_buffer_->Mean(); 117 return 0; 118 } 119 120 } // namespace webrtc 121