| /* |
| * 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 <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++) { |
| const float val = 0.99f / ((i + 1) * (j + 1)); |
| data[i].push_back(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 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]); |
| } |
| } |
| } |
| } |
| |
| // Tests gain applier. |
| TEST(IntelligibilityUtilsTest, TestGainApplier) { |
| const int kFreqs = 10; |
| const int kSamples = 100; |
| const float kChangeLimit = 0.1f; |
| GainApplier gain_applier(kFreqs, kChangeLimit); |
| const vector<vector<complex<float>>> in_data( |
| GenerateTestData(kFreqs, kSamples)); |
| vector<vector<complex<float>>> out_data(GenerateTestData(kFreqs, kSamples)); |
| for (int 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); |
| } |
| } |
| } |
| |
| } // namespace intelligibility |
| |
| } // namespace webrtc |