Integrate Intelligibility with APM

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"
 
 namespace webrtc {
 
 namespace {
 
 // Target output for ERB create test. Generated with matlab.
 const float kTestCenterFreqs[] = {
     13.169f, 26.965f, 41.423f, 56.577f, 72.461f, 89.113f, 106.57f, 124.88f,
@@ skipping 35 matching lines @@
     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.");
 const float kMaxTestError = 0.005f;
 
 // Enhancer initialization parameters.
 const int kSamples = 2000;
-const int kErbResolution = 2;
 const int kSampleRate = 1000;
+const int kNumChannels = 1;
 const int kFragmentSize = kSampleRate / 100;
-const int kNumChannels = 1;
-const float kDecayRate = 0.9f;
-const int kWindowSize = 800;
-const int kAnalyzeRate = 800;
-const int kVarianceRate = 2;
-const float kGainLimit = 0.1f;
+const float kNoiseDetected = 0.f;
+const float kSpeechDetected = 1.f;
 
 }  // namespace
 
 using std::vector;
 using intelligibility::VarianceArray;
 
 class IntelligibilityEnhancerTest : public ::testing::Test {
  protected:
   IntelligibilityEnhancerTest()
-      : enh_(kErbResolution,
-             kSampleRate,
-             kNumChannels,
-             VarianceArray::kStepInfinite,
-             kDecayRate,
-             kWindowSize,
-             kAnalyzeRate,
-             kVarianceRate,
-             kGainLimit),
-        clear_data_(kSamples),
-        noise_data_(kSamples),
-        orig_data_(kSamples) {}
+      : 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) {
-    IntelligibilityEnhancer enh(kErbResolution, kSampleRate, kNumChannels,
-                                step_type, kDecayRate, kWindowSize,
-                                kAnalyzeRate, kVarianceRate, kGainLimit);
+    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.ProcessCaptureAudio(&noise_cursor);
-      enh.ProcessRenderAudio(&clear_cursor);
+      enh_->AnalyzeCaptureAudio(&noise_cursor, kSampleRate, kNumChannels,
+                                kNoiseDetected);
+      enh_->ProcessRenderAudio(&clear_cursor, kSampleRate, kNumChannels,
+                               kSpeechDetected);
       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 enh_;
+  IntelligibilityEnhancer::Config config_;
+  rtc::scoped_ptr<IntelligibilityEnhancer> enh_;
   vector<float> clear_data_;
   vector<float> noise_data_;
   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.
 TEST_F(IntelligibilityEnhancerTest, TestRenderUpdate) {
   vector<VarianceArray::StepType> step_types;
   step_types.push_back(VarianceArray::kStepInfinite);
@@ skipping 15 matching lines @@
   }
   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));
   }
 }
 
 // Tests ERB bank creation, comparing against matlab output.
 TEST_F(IntelligibilityEnhancerTest, TestErbCreation) {
-  ASSERT_EQ(static_cast<int>(arraysize(kTestCenterFreqs)), enh_.bank_size_);
-  for (int i = 0; i < enh_.bank_size_; ++i) {
-    EXPECT_NEAR(kTestCenterFreqs[i], enh_.center_freqs_[i], kMaxTestError);
-    ASSERT_EQ(static_cast<int>(arraysize(kTestFilterBank[0])), enh_.freqs_);
-    for (int j = 0; j < enh_.freqs_; ++j) {
-      EXPECT_NEAR(kTestFilterBank[i][j], enh_.filter_bank_[i][j],
+  ASSERT_EQ(static_cast<int>(arraysize(kTestCenterFreqs)), enh_->bank_size_);
+  for (int i = 0; i < enh_->bank_size_; ++i) {
+    EXPECT_NEAR(kTestCenterFreqs[i], enh_->center_freqs_[i], kMaxTestError);
+    ASSERT_EQ(static_cast<int>(arraysize(kTestFilterBank[0])), enh_->freqs_);
+    for (int j = 0; j < enh_->freqs_; ++j) {
+      EXPECT_NEAR(kTestFilterBank[i][j], enh_->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_);
+  ASSERT_EQ(kTestStartFreq, enh_->start_freq_);
+  vector<float> sols(enh_->bank_size_);
   float lambda = -0.001f;
-  for (int i = 0; i < enh_.bank_size_; i++) {
-    enh_.filtered_clear_var_[i] = 0.0f;
-    enh_.filtered_noise_var_[i] = 0.0f;
-    enh_.rho_[i] = 0.02f;
+  for (int i = 0; i < enh_->bank_size_; i++) {
+    enh_->filtered_clear_var_[i] = 0.0f;
+    enh_->filtered_noise_var_[i] = 0.0f;
+    enh_->rho_[i] = 0.02f;
   }
-  enh_.SolveForGainsGivenLambda(lambda, enh_.start_freq_, &sols[0]);
-  for (int i = 0; i < enh_.bank_size_; i++) {
+  enh_->SolveForGainsGivenLambda(lambda, enh_->start_freq_, &sols[0]);
+  for (int i = 0; i < enh_->bank_size_; i++) {
     EXPECT_NEAR(kTestZeroVar[i], sols[i], kMaxTestError);
   }
-  for (int 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);
+  for (int 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_.SolveForGainsGivenLambda(lambda, enh_.start_freq_, &sols[0]);
-  for (int i = 0; i < enh_.bank_size_; i++) {
+  enh_->SolveForGainsGivenLambda(lambda, enh_->start_freq_, &sols[0]);
+  for (int 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 (int i = 0; i < enh_.bank_size_; i++) {
+  enh_->SolveForGainsGivenLambda(lambda, enh_->start_freq_, &sols[0]);
+  for (int i = 0; i < enh_->bank_size_; i++) {
     EXPECT_NEAR(kTestZeroVar[i], sols[i], kMaxTestError);
   }
 }
 
 }  // namespace webrtc