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