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 <math.h> 14 #include <string.h> 15 16 #include "webrtc/modules/audio_processing/vad/vad_circular_buffer.h" 17 #include "webrtc/modules/audio_processing/vad/common.h" 18 #include "webrtc/modules/audio_processing/vad/noise_gmm_tables.h" 19 #include "webrtc/modules/audio_processing/vad/voice_gmm_tables.h" 20 #include "webrtc/modules/include/module_common_types.h" 21 22 namespace webrtc { 23 24 static_assert(kNoiseGmmDim == kVoiceGmmDim, 25 "noise and voice gmm dimension not equal"); 26 27 // These values should match MATLAB counterparts for unit-tests to pass. 28 static const int kPosteriorHistorySize = 500; // 5 sec of 10 ms frames. 29 static const double kInitialPriorProbability = 0.3; 30 static const int kTransientWidthThreshold = 7; 31 static const double kLowProbabilityThreshold = 0.2; 32 LimitProbability(double p)33static double LimitProbability(double p) { 34 const double kLimHigh = 0.99; 35 const double kLimLow = 0.01; 36 37 if (p > kLimHigh) 38 p = kLimHigh; 39 else if (p < kLimLow) 40 p = kLimLow; 41 return p; 42 } 43 PitchBasedVad()44PitchBasedVad::PitchBasedVad() 45 : p_prior_(kInitialPriorProbability), 46 circular_buffer_(VadCircularBuffer::Create(kPosteriorHistorySize)) { 47 // Setup noise GMM. 48 noise_gmm_.dimension = kNoiseGmmDim; 49 noise_gmm_.num_mixtures = kNoiseGmmNumMixtures; 50 noise_gmm_.weight = kNoiseGmmWeights; 51 noise_gmm_.mean = &kNoiseGmmMean[0][0]; 52 noise_gmm_.covar_inverse = &kNoiseGmmCovarInverse[0][0][0]; 53 54 // Setup voice GMM. 55 voice_gmm_.dimension = kVoiceGmmDim; 56 voice_gmm_.num_mixtures = kVoiceGmmNumMixtures; 57 voice_gmm_.weight = kVoiceGmmWeights; 58 voice_gmm_.mean = &kVoiceGmmMean[0][0]; 59 voice_gmm_.covar_inverse = &kVoiceGmmCovarInverse[0][0][0]; 60 } 61 ~PitchBasedVad()62PitchBasedVad::~PitchBasedVad() { 63 } 64 VoicingProbability(const AudioFeatures & features,double * p_combined)65int 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 (size_t 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 / 94 (pdf_features_given_voice * p_prior_ + 95 pdf_features_given_noise * (1 - p_prior_)); 96 97 p = LimitProbability(p); 98 99 // Combine pitch-based probability with standalone probability, before 100 // updating prior probabilities. 101 double prod_active = p * p_combined[n]; 102 double prod_inactive = (1 - p) * (1 - p_combined[n]); 103 p_combined[n] = prod_active / (prod_active + prod_inactive); 104 105 if (UpdatePrior(p_combined[n]) < 0) 106 return -1; 107 // Limit prior probability. With a zero prior probability the posterior 108 // probability is always zero. 109 p_prior_ = LimitProbability(p_prior_); 110 } 111 return 0; 112 } 113 UpdatePrior(double p)114int PitchBasedVad::UpdatePrior(double p) { 115 circular_buffer_->Insert(p); 116 if (circular_buffer_->RemoveTransient(kTransientWidthThreshold, 117 kLowProbabilityThreshold) < 0) 118 return -1; 119 p_prior_ = circular_buffer_->Mean(); 120 return 0; 121 } 122 123 } // namespace webrtc 124