| /* |
| * 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, |
| 144.08f, 164.21f, 185.34f, 207.5f, 230.75f, 255.16f, 280.77f, 307.66f, |
| 335.9f, 365.56f, 396.71f, 429.44f, 463.84f, 500.f}; |
| const float kTestFilterBank[][2] = {{0.055556f, 0.f}, |
| {0.055556f, 0.f}, |
| {0.055556f, 0.f}, |
| {0.055556f, 0.f}, |
| {0.055556f, 0.f}, |
| {0.055556f, 0.f}, |
| {0.055556f, 0.f}, |
| {0.055556f, 0.f}, |
| {0.055556f, 0.f}, |
| {0.055556f, 0.f}, |
| {0.055556f, 0.f}, |
| {0.055556f, 0.f}, |
| {0.055556f, 0.f}, |
| {0.055556f, 0.f}, |
| {0.055556f, 0.f}, |
| {0.055556f, 0.f}, |
| {0.055556f, 0.f}, |
| {0.055556f, 0.2f}, |
| {0, 0.2f}, |
| {0, 0.2f}, |
| {0, 0.2f}, |
| {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."); |
| 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."); |
| 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) { |
| 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_->AnalyzeCaptureAudio(&noise_cursor, kSampleRate, kNumChannels); |
| 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_; |
| }; |
| |
| // 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); |
| 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::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)); |
| } |
| } |
| |
| // 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_->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_); |
| 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_->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_->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 |