Fix and simplify the power estimation in the IntelligibilityEnhancer

webrtc/modules/audio_processing/intelligibility/intelligibility_enhancer_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.
  */
 
-//
-//  Unit tests for intelligibility enhancer.
-//
-
 #include <math.h>
 #include <stdlib.h>
 #include <algorithm>
 #include <vector>
 
 #include "testing/gtest/include/gtest/gtest.h"
 #include "webrtc/base/arraysize.h"
 #include "webrtc/base/scoped_ptr.h"
 #include "webrtc/common_audio/signal_processing/include/signal_processing_library.h"
 #include "webrtc/modules/audio_processing/intelligibility/intelligibility_enhancer.h"
@@ skipping 31 matching lines @@
                                     {0, 0.2f}};
 static_assert(arraysize(kTestCenterFreqs) == arraysize(kTestFilterBank),
               "Test filterbank badly initialized.");
 
 // Target output for gain solving test. Generated with matlab.
 const size_t kTestStartFreq = 12;  // Lowest integral frequency for ERBs.
 const float kTestZeroVar[] = {1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f,
                               1.f, 1.f, 1.f, 0.f, 0.f, 0.f, 0.f, 0.f,
                               0.f, 0.f, 0.f, 0.f, 0.f, 0.f};
 static_assert(arraysize(kTestCenterFreqs) == arraysize(kTestZeroVar),
-              "Variance test data badly initialized.");
+              "Power test data badly initialized.");
 const float kTestNonZeroVarLambdaTop[] = {
     1.f,     1.f,     1.f,     1.f,     1.f,     1.f,     1.f,     1.f,
     1.f,     1.f,     1.f,     0.f,     0.f,     0.0351f, 0.0636f, 0.0863f,
     0.1037f, 0.1162f, 0.1236f, 0.1251f, 0.1189f, 0.0993f};
 static_assert(arraysize(kTestCenterFreqs) ==
                   arraysize(kTestNonZeroVarLambdaTop),
-              "Variance test data badly initialized.");
+              "Power test data badly initialized.");
 const float kMaxTestError = 0.005f;
 
 // Enhancer initialization parameters.
 const int kSamples = 2000;
 const int kSampleRate = 1000;
 const int kNumChannels = 1;
 const int kFragmentSize = kSampleRate / 100;
 
 }  // namespace
 
-using std::vector;
-using intelligibility::VarianceArray;
-
 class IntelligibilityEnhancerTest : public ::testing::Test {
  protected:
   IntelligibilityEnhancerTest()
       : clear_data_(kSamples), noise_data_(kSamples), orig_data_(kSamples) {
     config_.sample_rate_hz = kSampleRate;
     enh_.reset(new IntelligibilityEnhancer(config_));
   }
 
-  bool CheckUpdate(VarianceArray::StepType step_type) {
+  bool CheckUpdate() {
     config_.sample_rate_hz = kSampleRate;
-    config_.var_type = step_type;
     enh_.reset(new IntelligibilityEnhancer(config_));
     float* clear_cursor = &clear_data_[0];
     float* noise_cursor = &noise_data_[0];
     for (int i = 0; i < kSamples; i += kFragmentSize) {
       enh_->ProcessRenderAudio(&clear_cursor, kSampleRate, kNumChannels);
       clear_cursor += kFragmentSize;
       noise_cursor += kFragmentSize;
     }
     for (int i = 0; i < kSamples; i++) {
       if (std::fabs(clear_data_[i] - orig_data_[i]) > kMaxTestError) {
         return true;
       }
     }
     return false;
   }
 
   IntelligibilityEnhancer::Config config_;
   rtc::scoped_ptr<IntelligibilityEnhancer> enh_;
-  vector<float> clear_data_;
-  vector<float> noise_data_;
-  vector<float> orig_data_;
+  std::vector<float> clear_data_;
+  std::vector<float> noise_data_;
+  std::vector<float> orig_data_;
 };
 
-// For each class of generated data, tests that render stream is
-// updated when it should be for each variance update method.
+// For each class of generated data, tests that render stream is updated when
+// it should be.
 TEST_F(IntelligibilityEnhancerTest, TestRenderUpdate) {
-  vector<VarianceArray::StepType> step_types;
-  step_types.push_back(VarianceArray::kStepInfinite);
-  step_types.push_back(VarianceArray::kStepDecaying);
-  step_types.push_back(VarianceArray::kStepWindowed);
-  step_types.push_back(VarianceArray::kStepBlocked);
-  step_types.push_back(VarianceArray::kStepBlockBasedMovingAverage);
   std::fill(noise_data_.begin(), noise_data_.end(), 0.0f);
   std::fill(orig_data_.begin(), orig_data_.end(), 0.0f);
-  for (auto step_type : step_types) {
-    std::fill(clear_data_.begin(), clear_data_.end(), 0.0f);
-    EXPECT_FALSE(CheckUpdate(step_type));
-  }
+  std::fill(clear_data_.begin(), clear_data_.end(), 0.0f);
+  EXPECT_FALSE(CheckUpdate());
   std::srand(1);
   auto float_rand = []() { return std::rand() * 2.f / RAND_MAX - 1; };
   std::generate(noise_data_.begin(), noise_data_.end(), float_rand);
-  for (auto step_type : step_types) {
-    EXPECT_FALSE(CheckUpdate(step_type));
-  }
-  for (auto step_type : step_types) {
-    std::generate(clear_data_.begin(), clear_data_.end(), float_rand);
-    orig_data_ = clear_data_;
-    EXPECT_TRUE(CheckUpdate(step_type));
-  }
+  EXPECT_FALSE(CheckUpdate());
+  std::generate(clear_data_.begin(), clear_data_.end(), float_rand);
+  orig_data_ = clear_data_;
+  EXPECT_TRUE(CheckUpdate());
 }
 
 // Tests ERB bank creation, comparing against matlab output.
 TEST_F(IntelligibilityEnhancerTest, TestErbCreation) {
   ASSERT_EQ(arraysize(kTestCenterFreqs), enh_->bank_size_);
   for (size_t i = 0; i < enh_->bank_size_; ++i) {
     EXPECT_NEAR(kTestCenterFreqs[i], enh_->center_freqs_[i], kMaxTestError);
     ASSERT_EQ(arraysize(kTestFilterBank[0]), enh_->freqs_);
     for (size_t j = 0; j < enh_->freqs_; ++j) {
       EXPECT_NEAR(kTestFilterBank[i][j], enh_->render_filter_bank_[i][j],
                   kMaxTestError);
     }
   }
 }
 
 // Tests analytic solution for optimal gains, comparing
 // against matlab output.
 TEST_F(IntelligibilityEnhancerTest, TestSolveForGains) {
   ASSERT_EQ(kTestStartFreq, enh_->start_freq_);
-  vector<float> sols(enh_->bank_size_);
+  std::vector<float> sols(enh_->bank_size_);
   float lambda = -0.001f;
   for (size_t i = 0; i < enh_->bank_size_; i++) {
-    enh_->filtered_clear_var_[i] = 0.0f;
-    enh_->filtered_noise_var_[i] = 0.0f;
+    enh_->filtered_clear_pow_[i] = 0.0f;
+    enh_->filtered_noise_pow_[i] = 0.0f;
     enh_->rho_[i] = 0.02f;
   }
   enh_->SolveForGainsGivenLambda(lambda, enh_->start_freq_, &sols[0]);
   for (size_t i = 0; i < enh_->bank_size_; i++) {
     EXPECT_NEAR(kTestZeroVar[i], sols[i], kMaxTestError);
   }
   for (size_t i = 0; i < enh_->bank_size_; i++) {
-    enh_->filtered_clear_var_[i] = static_cast<float>(i + 1);
-    enh_->filtered_noise_var_[i] = static_cast<float>(enh_->bank_size_ - i);
+    enh_->filtered_clear_pow_[i] = static_cast<float>(i + 1);
+    enh_->filtered_noise_pow_[i] = static_cast<float>(enh_->bank_size_ - i);
   }
   enh_->SolveForGainsGivenLambda(lambda, enh_->start_freq_, &sols[0]);
   for (size_t i = 0; i < enh_->bank_size_; i++) {
     EXPECT_NEAR(kTestNonZeroVarLambdaTop[i], sols[i], kMaxTestError);
   }
   lambda = -1.0;
   enh_->SolveForGainsGivenLambda(lambda, enh_->start_freq_, &sols[0]);
   for (size_t i = 0; i < enh_->bank_size_; i++) {
     EXPECT_NEAR(kTestZeroVar[i], sols[i], kMaxTestError);
   }
 }
 
 }  // namespace webrtc