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

webrtc/modules/audio_processing/intelligibility/intelligibility_utils_unittest.cc

 /*
  *  Copyright (c) 2015 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 21 matching lines @@
   return data;
 }
 
 // Tests PowerEstimator, for all power step types.
 TEST(IntelligibilityUtilsTest, TestPowerEstimator) {
   const size_t kFreqs = 10;
   const size_t kSamples = 100;
   const float kDecay = 0.5f;
   const std::vector<std::vector<std::complex<float>>> test_data(
       GenerateTestData(kFreqs, kSamples));
-  PowerEstimator power_estimator(kFreqs, kDecay);
-  EXPECT_EQ(0, power_estimator.Power()[0]);
+  PowerEstimator<std::complex<float>> power_estimator(kFreqs, kDecay);
+  EXPECT_EQ(0, power_estimator.power()[0]);
 
   // Makes sure Step is doing something.
   power_estimator.Step(&test_data[0][0]);
   for (size_t i = 1; i < kSamples; ++i) {
     power_estimator.Step(&test_data[i][0]);
     for (size_t j = 0; j < kFreqs; ++j) {
-      const float* power = power_estimator.Power();
-      EXPECT_GE(power[j], 0.f);
-      EXPECT_LE(power[j], 1.f);
+      EXPECT_GE(power_estimator.power()[j], 0.f);
+      EXPECT_LE(power_estimator.power()[j], 1.f);
     }
   }
 }
 
 // Tests gain applier.
 TEST(IntelligibilityUtilsTest, TestGainApplier) {
   const size_t kFreqs = 10;
   const size_t kSamples = 100;
   const float kChangeLimit = 0.1f;
   GainApplier gain_applier(kFreqs, kChangeLimit);
   const std::vector<std::vector<std::complex<float>>> in_data(
       GenerateTestData(kFreqs, kSamples));
-  std::vector<std::vector<std::complex<float>>> out_data(GenerateTestData(
-      kFreqs, kSamples));
+  std::vector<std::vector<std::complex<float>>> out_data(
+      GenerateTestData(kFreqs, kSamples));
   for (size_t i = 0; i < kSamples; ++i) {
     gain_applier.Apply(&in_data[i][0], &out_data[i][0]);
     for (size_t j = 0; j < kFreqs; ++j) {
       EXPECT_GT(out_data[i][j].real(), 0.f);
       EXPECT_LT(out_data[i][j].real(), 1.f);
       EXPECT_GT(out_data[i][j].imag(), 0.f);
       EXPECT_LT(out_data[i][j].imag(), 1.f);
     }
   }
 }
 
 }  // namespace intelligibility
 
 }  // namespace webrtc