Use VAD to get a better speech power estimation in the IntelligibilityEnhancer

webrtc/modules/audio_processing/noise_suppression_impl.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.
  */
 
@@ skipping 164 matching lines @@
 
 std::vector<float> NoiseSuppressionImpl::NoiseEstimate() {
   rtc::CritScope cs(crit_);
   std::vector<float> noise_estimate;
 #if defined(WEBRTC_NS_FLOAT)
   const float kNormalizationFactor = 1.f / (1 << 15);
   noise_estimate.assign(WebRtcNs_num_freq(), 0.f);
   for (auto& suppressor : suppressors_) {
     const float* noise = WebRtcNs_noise_estimate(suppressor->state());
     for (size_t i = 0; i < noise_estimate.size(); ++i) {
-      noise_estimate[i] += kNormalizationFactor *
-          noise[i] / suppressors_.size();
+      noise_estimate[i] +=
+          kNormalizationFactor * noise[i] / suppressors_.size();
     }
   }
 #elif defined(WEBRTC_NS_FIXED)
   const float kNormalizationFactor = 1.f / (1 << 23);
   noise_estimate.assign(WebRtcNsx_num_freq(), 0.f);
   for (auto& suppressor : suppressors_) {
     const uint32_t* noise = WebRtcNsx_noise_estimate(suppressor->state());
     for (size_t i = 0; i < noise_estimate.size(); ++i) {
       noise_estimate[i] += kNormalizationFactor *
           static_cast<float>(noise[i]) / suppressors_.size();
     }
   }
 #endif
   return noise_estimate;
 }
 
 }  // namespace webrtc