Pull the Voice Activity Detector out from the AGC

webrtc/modules/audio_processing/vad/pitch_based_vad.cc

 /*
  *  Copyright (c) 2012 The WebRTC project authors. All Rights Reserved.
  *
  *  Use of this source code is governed by a BSD-style license
  *  that can be found in the LICENSE file in the root of the source
  *  tree. An additional intellectual property rights grant can be found
  *  in the file PATENTS.  All contributing project authors may
  *  be found in the AUTHORS file in the root of the source tree.
  */
 
-#include "webrtc/modules/audio_processing/agc/pitch_based_vad.h"
+#include "webrtc/modules/audio_processing/vad/pitch_based_vad.h"
 
 #include <assert.h>
 #include <math.h>
 #include <string.h>
 
-#include "webrtc/modules/audio_processing/agc/circular_buffer.h"
-#include "webrtc/modules/audio_processing/agc/common.h"
-#include "webrtc/modules/audio_processing/agc/noise_gmm_tables.h"
-#include "webrtc/modules/audio_processing/agc/voice_gmm_tables.h"
+#include "webrtc/modules/audio_processing/vad/vad_circular_buffer.h"
+#include "webrtc/modules/audio_processing/vad/common.h"
+#include "webrtc/modules/audio_processing/vad/noise_gmm_tables.h"
+#include "webrtc/modules/audio_processing/vad/voice_gmm_tables.h"
 #include "webrtc/modules/interface/module_common_types.h"
 
 namespace webrtc {
 
 static_assert(kNoiseGmmDim == kVoiceGmmDim,
               "noise and voice gmm dimension not equal");
 
 // These values should match MATLAB counterparts for unit-tests to pass.
 static const int kPosteriorHistorySize = 500;  // 5 sec of 10 ms frames.
 static const double kInitialPriorProbability = 0.3;
 static const int kTransientWidthThreshold = 7;
 static const double kLowProbabilityThreshold = 0.2;
 
 static double LimitProbability(double p) {
   const double kLimHigh = 0.99;
   const double kLimLow = 0.01;
 
   if (p > kLimHigh)
     p = kLimHigh;
   else if (p < kLimLow)
     p = kLimLow;
   return p;
 }
 
 PitchBasedVad::PitchBasedVad()
     : p_prior_(kInitialPriorProbability),
-      circular_buffer_(AgcCircularBuffer::Create(kPosteriorHistorySize)) {
+      circular_buffer_(VadCircularBuffer::Create(kPosteriorHistorySize)) {
   // Setup noise GMM.
   noise_gmm_.dimension = kNoiseGmmDim;
   noise_gmm_.num_mixtures = kNoiseGmmNumMixtures;
   noise_gmm_.weight = kNoiseGmmWeights;
   noise_gmm_.mean = &kNoiseGmmMean[0][0];
   noise_gmm_.covar_inverse = &kNoiseGmmCovarInverse[0][0][0];
 
   // Setup voice GMM.
   voice_gmm_.dimension = kVoiceGmmDim;
   voice_gmm_.num_mixtures = kVoiceGmmNumMixtures;
   voice_gmm_.weight = kVoiceGmmWeights;
   voice_gmm_.mean = &kVoiceGmmMean[0][0];
   voice_gmm_.covar_inverse = &kVoiceGmmCovarInverse[0][0][0];
 }
 
-PitchBasedVad::~PitchBasedVad() {}
+PitchBasedVad::~PitchBasedVad() {
+}
 
 int PitchBasedVad::VoicingProbability(const AudioFeatures& features,
                                       double* p_combined) {
   double p;
   double gmm_features[3];
   double pdf_features_given_voice;
   double pdf_features_given_noise;
   // These limits are the same in matlab implementation 'VoicingProbGMM().'
   const double kLimLowLogPitchGain = -2.0;
   const double kLimHighLogPitchGain = -0.9;
   const double kLimLowSpectralPeak = 200;
   const double kLimHighSpectralPeak = 2000;
   const double kEps = 1e-12;
   for (int n = 0; n < features.num_frames; n++) {
     gmm_features[0] = features.log_pitch_gain[n];
     gmm_features[1] = features.spectral_peak[n];
     gmm_features[2] = features.pitch_lag_hz[n];
 
     pdf_features_given_voice = EvaluateGmm(gmm_features, voice_gmm_);
     pdf_features_given_noise = EvaluateGmm(gmm_features, noise_gmm_);
 
     if (features.spectral_peak[n] < kLimLowSpectralPeak ||
         features.spectral_peak[n] > kLimHighSpectralPeak ||
         features.log_pitch_gain[n] < kLimLowLogPitchGain) {
       pdf_features_given_voice = kEps * pdf_features_given_noise;
     } else if (features.log_pitch_gain[n] > kLimHighLogPitchGain) {
       pdf_features_given_noise = kEps * pdf_features_given_voice;
     }
 
-    p = p_prior_ * pdf_features_given_voice / (pdf_features_given_voice *
-        p_prior_ + pdf_features_given_noise * (1 - p_prior_));
+    p = p_prior_ * pdf_features_given_voice /
+        (pdf_features_given_voice * p_prior_ +
+         pdf_features_given_noise * (1 - p_prior_));
 
     p = LimitProbability(p);
 
     // Combine pitch-based probability with standalone probability, before
     // updating prior probabilities.
     double prod_active = p * p_combined[n];
     double prod_inactive = (1 - p) * (1 - p_combined[n]);
     p_combined[n] = prod_active / (prod_active + prod_inactive);
 
     if (UpdatePrior(p_combined[n]) < 0)
       return -1;
     // Limit prior probability. With a zero prior probability the posterior
     // probability is always zero.
     p_prior_ = LimitProbability(p_prior_);
   }
   return 0;
 }
 
 int PitchBasedVad::UpdatePrior(double p) {
   circular_buffer_->Insert(p);
   if (circular_buffer_->RemoveTransient(kTransientWidthThreshold,
                                         kLowProbabilityThreshold) < 0)
     return -1;
   p_prior_ = circular_buffer_->Mean();
   return 0;
 }
 
 }  // namespace webrtc