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.
  */
 
-//
-//  Unit tests for intelligibility utils.
-//
-
-#include <math.h>
+#include <cmath>
 #include <complex>
-#include <iostream>
 #include <vector>
 
 #include "testing/gtest/include/gtest/gtest.h"
 #include "webrtc/base/arraysize.h"
 #include "webrtc/modules/audio_processing/intelligibility/intelligibility_utils.h"
 
-using std::complex;
-using std::vector;
-
 namespace webrtc {
 
 namespace intelligibility {
 
-vector<vector<complex<float>>> GenerateTestData(int freqs, int samples) {
-  vector<vector<complex<float>>> data(samples);
-  for (int i = 0; i < samples; i++) {
-    for (int j = 0; j < freqs; j++) {
+std::vector<std::vector<std::complex<float>>> GenerateTestData(size_t freqs,
+                                                               size_t samples) {
+  std::vector<std::vector<std::complex<float>>> data(samples);
+  for (size_t i = 0; i < samples; ++i) {
+    for (size_t j = 0; j < freqs; ++j) {
       const float val = 0.99f / ((i + 1) * (j + 1));
-      data[i].push_back(complex<float>(val, val));
+      data[i].push_back(std::complex<float>(val, val));
     }
   }
   return data;
 }
 
-// Tests UpdateFactor.
-TEST(IntelligibilityUtilsTest, TestUpdateFactor) {
-  EXPECT_EQ(0, intelligibility::UpdateFactor(0, 0, 0));
-  EXPECT_EQ(4, intelligibility::UpdateFactor(4, 2, 3));
-  EXPECT_EQ(3, intelligibility::UpdateFactor(4, 2, 1));
-  EXPECT_EQ(2, intelligibility::UpdateFactor(2, 4, 3));
-  EXPECT_EQ(3, intelligibility::UpdateFactor(2, 4, 1));
-}
+// 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]);
 
-// Tests zerofudge.
-TEST(IntelligibilityUtilsTest, TestCplx) {
-  complex<float> t0(1.f, 0.f);
-  t0 = intelligibility::zerofudge(t0);
-  EXPECT_NE(t0.imag(), 0.f);
-  EXPECT_NE(t0.real(), 0.f);
-}
-
-// Tests NewMean and AddToMean.
-TEST(IntelligibilityUtilsTest, TestMeanUpdate) {
-  const complex<float> data[] = {{3, 8}, {7, 6}, {2, 1}, {8, 9}, {0, 6}};
-  const complex<float> means[] = {{3, 8}, {5, 7}, {4, 5}, {5, 6}, {4, 6}};
-  complex<float> mean(3, 8);
-  for (size_t i = 0; i < arraysize(data); i++) {
-    EXPECT_EQ(means[i], NewMean(mean, data[i], i + 1));
-    AddToMean(data[i], i + 1, &mean);
-    EXPECT_EQ(means[i], mean);
-  }
-}
-
-// Tests VarianceArray, for all variance step types.
-TEST(IntelligibilityUtilsTest, TestVarianceArray) {
-  const int kFreqs = 10;
-  const int kSamples = 100;
-  const int kWindowSize = 10;  // Should pass for all kWindowSize > 1.
-  const float kDecay = 0.5f;
-  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);
-  const vector<vector<complex<float>>> test_data(
-      GenerateTestData(kFreqs, kSamples));
-  for (auto step_type : step_types) {
-    VarianceArray variance_array(kFreqs, step_type, kWindowSize, kDecay);
-    EXPECT_EQ(0, variance_array.variance()[0]);
-    EXPECT_EQ(0, variance_array.array_mean());
-    variance_array.ApplyScale(2.0f);
-    EXPECT_EQ(0, variance_array.variance()[0]);
-    EXPECT_EQ(0, variance_array.array_mean());
-
-    // Makes sure Step is doing something.
-    variance_array.Step(&test_data[0][0]);
-    for (int i = 1; i < kSamples; i++) {
-      variance_array.Step(&test_data[i][0]);
-      EXPECT_GE(variance_array.array_mean(), 0.0f);
-      EXPECT_LE(variance_array.array_mean(), 1.0f);
-      for (int j = 0; j < kFreqs; j++) {
-        EXPECT_GE(variance_array.variance()[j], 0.0f);
-        EXPECT_LE(variance_array.variance()[j], 1.0f);
-      }
-    }
-    variance_array.Clear();
-    EXPECT_EQ(0, variance_array.variance()[0]);
-    EXPECT_EQ(0, variance_array.array_mean());
-  }
-}
-
-// Tests exact computation on synthetic data.
-TEST(IntelligibilityUtilsTest, TestMovingBlockAverage) {
-  // Exact, not unbiased estimates.
-  const float kTestVarianceBufferNotFull = 16.5f;
-  const float kTestVarianceBufferFull1 = 66.5f;
-  const float kTestVarianceBufferFull2 = 333.375f;
-  const int kFreqs = 2;
-  const int kSamples = 50;
-  const int kWindowSize = 2;
-  const float kDecay = 0.5f;
-  const float kMaxError = 0.0001f;
-
-  VarianceArray variance_array(
-      kFreqs, VarianceArray::kStepBlockBasedMovingAverage, kWindowSize, kDecay);
-
-  vector<vector<complex<float>>> test_data(kSamples);
-  for (int i = 0; i < kSamples; i++) {
-    for (int j = 0; j < kFreqs; j++) {
-      if (i < 30) {
-        test_data[i].push_back(complex<float>(static_cast<float>(kSamples - i),
-                                              static_cast<float>(i + 1)));
-      } else {
-        test_data[i].push_back(complex<float>(0.f, 0.f));
-      }
-    }
-  }
-
-  for (int i = 0; i < kSamples; i++) {
-    variance_array.Step(&test_data[i][0]);
-    for (int j = 0; j < kFreqs; j++) {
-      if (i < 9) {  // In utils, kWindowBlockSize = 10.
-        EXPECT_EQ(0, variance_array.variance()[j]);
-      } else if (i < 19) {
-        EXPECT_NEAR(kTestVarianceBufferNotFull, variance_array.variance()[j],
-                    kMaxError);
-      } else if (i < 39) {
-        EXPECT_NEAR(kTestVarianceBufferFull1, variance_array.variance()[j],
-                    kMaxError);
-      } else if (i < 49) {
-        EXPECT_NEAR(kTestVarianceBufferFull2, variance_array.variance()[j],
-                    kMaxError);
-      } else {
-        EXPECT_EQ(0, variance_array.variance()[j]);
-      }
+  // 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) {
+      EXPECT_GE(power_estimator.power()[j], 0.f);
+      EXPECT_LE(power_estimator.power()[j], 1.f);
     }
   }
 }
 
 // Tests gain applier.
 TEST(IntelligibilityUtilsTest, TestGainApplier) {
-  const int kFreqs = 10;
-  const int kSamples = 100;
+  const size_t kFreqs = 10;
+  const size_t kSamples = 100;
   const float kChangeLimit = 0.1f;
   GainApplier gain_applier(kFreqs, kChangeLimit);
-  const vector<vector<complex<float>>> in_data(
+  const std::vector<std::vector<std::complex<float>>> in_data(
       GenerateTestData(kFreqs, kSamples));
-  vector<vector<complex<float>>> out_data(GenerateTestData(kFreqs, kSamples));
-  for (int i = 0; i < kSamples; i++) {
+  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 (int j = 0; j < kFreqs; j++) {
-      EXPECT_GT(out_data[i][j].real(), 0.0f);
-      EXPECT_LT(out_data[i][j].real(), 1.0f);
-      EXPECT_GT(out_data[i][j].imag(), 0.0f);
-      EXPECT_LT(out_data[i][j].imag(), 1.0f);
+    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