83 lines
2.9 KiB
C++
83 lines
2.9 KiB
C++
/*
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* Copyright (c) 2018 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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#include "modules/audio_processing/agc2/signal_classifier.h"
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#include <array>
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#include <functional>
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#include <limits>
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#include "modules/audio_processing/agc2/agc2_testing_common.h"
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#include "modules/audio_processing/logging/apm_data_dumper.h"
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#include "rtc_base/gunit.h"
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#include "rtc_base/random.h"
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namespace webrtc {
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namespace {
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Random rand_gen(42);
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ApmDataDumper data_dumper(0);
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constexpr int kNumIterations = 100;
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// Runs the signal classifier on audio generated by 'sample_generator'
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// for kNumIterations. Returns the number of frames classified as noise.
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int RunClassifier(std::function<float()> sample_generator, int rate) {
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SignalClassifier classifier(&data_dumper);
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std::array<float, 480> signal;
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classifier.Initialize(rate);
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const size_t samples_per_channel = rtc::CheckedDivExact(rate, 100);
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int number_of_noise_frames = 0;
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for (int i = 0; i < kNumIterations; ++i) {
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for (size_t j = 0; j < samples_per_channel; ++j) {
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signal[j] = sample_generator();
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}
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number_of_noise_frames +=
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classifier.Analyze({&signal[0], samples_per_channel}) ==
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SignalClassifier::SignalType::kStationary;
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}
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return number_of_noise_frames;
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}
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float WhiteNoiseGenerator() {
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return static_cast<float>(rand_gen.Rand(std::numeric_limits<int16_t>::min(),
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std::numeric_limits<int16_t>::max()));
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}
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} // namespace
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// White random noise is stationary, but does not trigger the detector
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// every frame due to the randomness.
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TEST(AutomaticGainController2SignalClassifier, WhiteNoise) {
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for (const auto rate : {8000, 16000, 32000, 48000}) {
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const int number_of_noise_frames = RunClassifier(WhiteNoiseGenerator, rate);
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EXPECT_GT(number_of_noise_frames, kNumIterations / 2);
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}
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}
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// Sine curves are (very) stationary. They trigger the detector all
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// the time. Except for a few initial frames.
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TEST(AutomaticGainController2SignalClassifier, SineTone) {
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for (const auto rate : {8000, 16000, 32000, 48000}) {
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test::SineGenerator gen(600.f, rate);
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const int number_of_noise_frames = RunClassifier(gen, rate);
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EXPECT_GE(number_of_noise_frames, kNumIterations - 5);
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}
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}
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// Pulses are transient if they are far enough apart. They shouldn't
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// trigger the noise detector.
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TEST(AutomaticGainController2SignalClassifier, PulseTone) {
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for (const auto rate : {8000, 16000, 32000, 48000}) {
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test::PulseGenerator gen(30.f, rate);
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const int number_of_noise_frames = RunClassifier(gen, rate);
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EXPECT_EQ(number_of_noise_frames, 0);
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}
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}
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} // namespace webrtc
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