1 /*
2 * Copyright (c) 2018 The WebRTC project authors. All Rights Reserved.
3 *
4 * Use of this source code is governed by a BSD-style license
5 * that can be found in the LICENSE file in the root of the source
6 * tree. An additional intellectual property rights grant can be found
7 * in the file PATENTS. All contributing project authors may
8 * be found in the AUTHORS file in the root of the source tree.
9 */
10
11 #include "modules/audio_processing/agc2/signal_classifier.h"
12
13 #include <array>
14 #include <functional>
15 #include <limits>
16
17 #include "modules/audio_processing/agc2/agc2_testing_common.h"
18 #include "modules/audio_processing/logging/apm_data_dumper.h"
19 #include "rtc_base/gunit.h"
20 #include "rtc_base/random.h"
21
22 namespace webrtc {
23
24 namespace {
25 Random rand_gen(42);
26 ApmDataDumper data_dumper(0);
27 constexpr int kNumIterations = 100;
28
29 // Runs the signal classifier on audio generated by 'sample_generator'
30 // for kNumIterations. Returns the number of frames classified as noise.
RunClassifier(std::function<float ()> sample_generator,int rate)31 int RunClassifier(std::function<float()> sample_generator, int rate) {
32 SignalClassifier classifier(&data_dumper);
33 std::array<float, 480> signal;
34 classifier.Initialize(rate);
35 const size_t samples_per_channel = rtc::CheckedDivExact(rate, 100);
36 int number_of_noise_frames = 0;
37 for (int i = 0; i < kNumIterations; ++i) {
38 for (size_t j = 0; j < samples_per_channel; ++j) {
39 signal[j] = sample_generator();
40 }
41 number_of_noise_frames +=
42 classifier.Analyze({&signal[0], samples_per_channel}) ==
43 SignalClassifier::SignalType::kStationary;
44 }
45 return number_of_noise_frames;
46 }
47
WhiteNoiseGenerator()48 float WhiteNoiseGenerator() {
49 return static_cast<float>(rand_gen.Rand(std::numeric_limits<int16_t>::min(),
50 std::numeric_limits<int16_t>::max()));
51 }
52 } // namespace
53
54 // White random noise is stationary, but does not trigger the detector
55 // every frame due to the randomness.
TEST(AutomaticGainController2SignalClassifier,WhiteNoise)56 TEST(AutomaticGainController2SignalClassifier, WhiteNoise) {
57 for (const auto rate : {8000, 16000, 32000, 48000}) {
58 const int number_of_noise_frames = RunClassifier(WhiteNoiseGenerator, rate);
59 EXPECT_GT(number_of_noise_frames, kNumIterations / 2);
60 }
61 }
62
63 // Sine curves are (very) stationary. They trigger the detector all
64 // the time. Except for a few initial frames.
TEST(AutomaticGainController2SignalClassifier,SineTone)65 TEST(AutomaticGainController2SignalClassifier, SineTone) {
66 for (const auto rate : {8000, 16000, 32000, 48000}) {
67 test::SineGenerator gen(600.f, rate);
68 const int number_of_noise_frames = RunClassifier(gen, rate);
69 EXPECT_GE(number_of_noise_frames, kNumIterations - 5);
70 }
71 }
72
73 // Pulses are transient if they are far enough apart. They shouldn't
74 // trigger the noise detector.
TEST(AutomaticGainController2SignalClassifier,PulseTone)75 TEST(AutomaticGainController2SignalClassifier, PulseTone) {
76 for (const auto rate : {8000, 16000, 32000, 48000}) {
77 test::PulseGenerator gen(30.f, rate);
78 const int number_of_noise_frames = RunClassifier(gen, rate);
79 EXPECT_EQ(number_of_noise_frames, 0);
80 }
81 }
82 } // namespace webrtc
83