35b72fbceb
- New option for computing variance that is more adaptive with lower complexity. - Fixed related off-by-one errors. - Added intelligibility unittests. - Do not enhance if experiencing variance underflow. R=andrew@webrtc.org, henrik.lundin@webrtc.org Review URL: https://codereview.webrtc.org/1207353002 . Cr-Commit-Position: refs/heads/master@{#9567}
206 lines
7.8 KiB
C++
206 lines
7.8 KiB
C++
/*
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* Copyright (c) 2015 The WebRTC project authors. All Rights Reserved.
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*
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* Use of this source code is governed by a BSD-style license
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* that can be found in the LICENSE file in the root of the source
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* tree. An additional intellectual property rights grant can be found
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* in the file PATENTS. All contributing project authors may
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* be found in the AUTHORS file in the root of the source tree.
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*/
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//
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// Unit tests for intelligibility enhancer.
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//
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#include <math.h>
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#include <stdlib.h>
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#include <algorithm>
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#include <vector>
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#include "testing/gtest/include/gtest/gtest.h"
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#include "webrtc/base/arraysize.h"
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#include "webrtc/common_audio/signal_processing/include/signal_processing_library.h"
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#include "webrtc/modules/audio_processing/intelligibility/intelligibility_enhancer.h"
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namespace webrtc {
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namespace {
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// Target output for ERB create test. Generated with matlab.
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const float kTestCenterFreqs[] = {
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13.169f, 26.965f, 41.423f, 56.577f, 72.461f, 89.113f, 106.57f, 124.88f,
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144.08f, 164.21f, 185.34f, 207.5f, 230.75f, 255.16f, 280.77f, 307.66f,
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335.9f, 365.56f, 396.71f, 429.44f, 463.84f, 500.f};
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const float kTestFilterBank[][2] = {{0.055556f, 0.f},
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{0.055556f, 0.f},
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{0.055556f, 0.f},
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{0.055556f, 0.f},
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{0.055556f, 0.f},
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{0.055556f, 0.f},
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{0.055556f, 0.f},
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{0.055556f, 0.f},
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{0.055556f, 0.f},
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{0.055556f, 0.f},
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{0.055556f, 0.f},
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{0.055556f, 0.f},
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{0.055556f, 0.f},
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{0.055556f, 0.f},
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{0.055556f, 0.f},
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{0.055556f, 0.f},
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{0.055556f, 0.f},
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{0.055556f, 0.2f},
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{0, 0.2f},
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{0, 0.2f},
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{0, 0.2f},
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{0, 0.2f}};
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static_assert(arraysize(kTestCenterFreqs) == arraysize(kTestFilterBank),
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"Test filterbank badly initialized.");
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// Target output for gain solving test. Generated with matlab.
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const int kTestStartFreq = 12; // Lowest integral frequency for ERBs.
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const float kTestZeroVar[] = {1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f,
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1.f, 1.f, 1.f, 0.f, 0.f, 0.f, 0.f, 0.f,
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0.f, 0.f, 0.f, 0.f, 0.f, 0.f};
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static_assert(arraysize(kTestCenterFreqs) == arraysize(kTestZeroVar),
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"Variance test data badly initialized.");
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const float kTestNonZeroVarLambdaTop[] = {
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1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f,
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1.f, 1.f, 1.f, 0.f, 0.f, 0.0351f, 0.0636f, 0.0863f,
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0.1037f, 0.1162f, 0.1236f, 0.1251f, 0.1189f, 0.0993f};
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static_assert(arraysize(kTestCenterFreqs) ==
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arraysize(kTestNonZeroVarLambdaTop),
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"Variance test data badly initialized.");
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const float kMaxTestError = 0.005f;
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// Enhancer initialization parameters.
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const int kSamples = 2000;
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const int kErbResolution = 2;
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const int kSampleRate = 1000;
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const int kFragmentSize = kSampleRate / 100;
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const int kNumChannels = 1;
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const float kDecayRate = 0.9f;
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const int kWindowSize = 800;
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const int kAnalyzeRate = 800;
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const int kVarianceRate = 2;
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const float kGainLimit = 0.1f;
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} // namespace
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using std::vector;
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using intelligibility::VarianceArray;
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class IntelligibilityEnhancerTest : public ::testing::Test {
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protected:
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IntelligibilityEnhancerTest()
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: enh_(kErbResolution,
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kSampleRate,
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kNumChannels,
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VarianceArray::kStepInfinite,
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kDecayRate,
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kWindowSize,
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kAnalyzeRate,
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kVarianceRate,
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kGainLimit),
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clear_data_(kSamples),
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noise_data_(kSamples),
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orig_data_(kSamples) {}
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bool CheckUpdate(VarianceArray::StepType step_type) {
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IntelligibilityEnhancer enh(kErbResolution, kSampleRate, kNumChannels,
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step_type, kDecayRate, kWindowSize,
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kAnalyzeRate, kVarianceRate, kGainLimit);
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float* clear_cursor = &clear_data_[0];
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float* noise_cursor = &noise_data_[0];
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for (int i = 0; i < kSamples; i += kFragmentSize) {
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enh.ProcessCaptureAudio(&noise_cursor);
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enh.ProcessRenderAudio(&clear_cursor);
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clear_cursor += kFragmentSize;
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noise_cursor += kFragmentSize;
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}
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for (int i = 0; i < kSamples; i++) {
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if (std::fabs(clear_data_[i] - orig_data_[i]) > kMaxTestError) {
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return true;
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}
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}
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return false;
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}
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IntelligibilityEnhancer enh_;
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vector<float> clear_data_;
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vector<float> noise_data_;
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vector<float> orig_data_;
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};
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// For each class of generated data, tests that render stream is
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// updated when it should be for each variance update method.
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TEST_F(IntelligibilityEnhancerTest, TestRenderUpdate) {
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vector<VarianceArray::StepType> step_types;
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step_types.push_back(VarianceArray::kStepInfinite);
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step_types.push_back(VarianceArray::kStepDecaying);
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step_types.push_back(VarianceArray::kStepWindowed);
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step_types.push_back(VarianceArray::kStepBlocked);
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step_types.push_back(VarianceArray::kStepBlockBasedMovingAverage);
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std::fill(noise_data_.begin(), noise_data_.end(), 0.0f);
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std::fill(orig_data_.begin(), orig_data_.end(), 0.0f);
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for (auto step_type : step_types) {
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std::fill(clear_data_.begin(), clear_data_.end(), 0.0f);
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EXPECT_FALSE(CheckUpdate(step_type));
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}
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std::srand(1);
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auto float_rand = []() { return std::rand() * 2.f / RAND_MAX - 1; };
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std::generate(noise_data_.begin(), noise_data_.end(), float_rand);
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for (auto step_type : step_types) {
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EXPECT_FALSE(CheckUpdate(step_type));
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}
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for (auto step_type : step_types) {
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std::generate(clear_data_.begin(), clear_data_.end(), float_rand);
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orig_data_ = clear_data_;
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EXPECT_TRUE(CheckUpdate(step_type));
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}
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}
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// Tests ERB bank creation, comparing against matlab output.
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TEST_F(IntelligibilityEnhancerTest, TestErbCreation) {
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ASSERT_EQ(static_cast<int>(arraysize(kTestCenterFreqs)), enh_.bank_size_);
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for (int i = 0; i < enh_.bank_size_; ++i) {
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EXPECT_NEAR(kTestCenterFreqs[i], enh_.center_freqs_[i], kMaxTestError);
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ASSERT_EQ(static_cast<int>(arraysize(kTestFilterBank[0])), enh_.freqs_);
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for (int j = 0; j < enh_.freqs_; ++j) {
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EXPECT_NEAR(kTestFilterBank[i][j], enh_.filter_bank_[i][j],
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kMaxTestError);
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}
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}
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}
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// Tests analytic solution for optimal gains, comparing
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// against matlab output.
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TEST_F(IntelligibilityEnhancerTest, TestSolveForGains) {
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ASSERT_EQ(kTestStartFreq, enh_.start_freq_);
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vector<float> sols(enh_.bank_size_);
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float lambda = -0.001f;
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for (int i = 0; i < enh_.bank_size_; i++) {
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enh_.filtered_clear_var_[i] = 0.0f;
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enh_.filtered_noise_var_[i] = 0.0f;
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enh_.rho_[i] = 0.02f;
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}
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enh_.SolveForGainsGivenLambda(lambda, enh_.start_freq_, &sols[0]);
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for (int i = 0; i < enh_.bank_size_; i++) {
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EXPECT_NEAR(kTestZeroVar[i], sols[i], kMaxTestError);
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}
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for (int i = 0; i < enh_.bank_size_; i++) {
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enh_.filtered_clear_var_[i] = static_cast<float>(i + 1);
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enh_.filtered_noise_var_[i] = static_cast<float>(enh_.bank_size_ - i);
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}
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enh_.SolveForGainsGivenLambda(lambda, enh_.start_freq_, &sols[0]);
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for (int i = 0; i < enh_.bank_size_; i++) {
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EXPECT_NEAR(kTestNonZeroVarLambdaTop[i], sols[i], kMaxTestError);
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}
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lambda = -1.0;
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enh_.SolveForGainsGivenLambda(lambda, enh_.start_freq_, &sols[0]);
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for (int i = 0; i < enh_.bank_size_; i++) {
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EXPECT_NEAR(kTestZeroVar[i], sols[i], kMaxTestError);
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}
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}
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} // namespace webrtc
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