Update to current webrtc library
This is from the upstream library commit id 3326535126e435f1ba647885ce43a8f0f3d317eb, corresponding to Chromium 88.0.4290.1.
This commit is contained in:
177
webrtc/modules/audio_processing/agc2/signal_classifier.cc
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177
webrtc/modules/audio_processing/agc2/signal_classifier.cc
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/*
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* Copyright (c) 2016 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 <algorithm>
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#include <numeric>
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#include <vector>
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#include "api/array_view.h"
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#include "modules/audio_processing/agc2/down_sampler.h"
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#include "modules/audio_processing/agc2/noise_spectrum_estimator.h"
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#include "modules/audio_processing/logging/apm_data_dumper.h"
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#include "rtc_base/checks.h"
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#include "system_wrappers/include/cpu_features_wrapper.h"
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namespace webrtc {
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namespace {
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bool IsSse2Available() {
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#if defined(WEBRTC_ARCH_X86_FAMILY)
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return GetCPUInfo(kSSE2) != 0;
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#else
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return false;
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#endif
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}
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void RemoveDcLevel(rtc::ArrayView<float> x) {
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RTC_DCHECK_LT(0, x.size());
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float mean = std::accumulate(x.data(), x.data() + x.size(), 0.f);
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mean /= x.size();
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for (float& v : x) {
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v -= mean;
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}
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}
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void PowerSpectrum(const OouraFft* ooura_fft,
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rtc::ArrayView<const float> x,
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rtc::ArrayView<float> spectrum) {
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RTC_DCHECK_EQ(65, spectrum.size());
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RTC_DCHECK_EQ(128, x.size());
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float X[128];
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std::copy(x.data(), x.data() + x.size(), X);
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ooura_fft->Fft(X);
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float* X_p = X;
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RTC_DCHECK_EQ(X_p, &X[0]);
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spectrum[0] = (*X_p) * (*X_p);
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++X_p;
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RTC_DCHECK_EQ(X_p, &X[1]);
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spectrum[64] = (*X_p) * (*X_p);
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for (int k = 1; k < 64; ++k) {
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++X_p;
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RTC_DCHECK_EQ(X_p, &X[2 * k]);
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spectrum[k] = (*X_p) * (*X_p);
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++X_p;
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RTC_DCHECK_EQ(X_p, &X[2 * k + 1]);
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spectrum[k] += (*X_p) * (*X_p);
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}
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}
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webrtc::SignalClassifier::SignalType ClassifySignal(
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rtc::ArrayView<const float> signal_spectrum,
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rtc::ArrayView<const float> noise_spectrum,
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ApmDataDumper* data_dumper) {
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int num_stationary_bands = 0;
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int num_highly_nonstationary_bands = 0;
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// Detect stationary and highly nonstationary bands.
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for (size_t k = 1; k < 40; k++) {
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if (signal_spectrum[k] < 3 * noise_spectrum[k] &&
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signal_spectrum[k] * 3 > noise_spectrum[k]) {
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++num_stationary_bands;
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} else if (signal_spectrum[k] > 9 * noise_spectrum[k]) {
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++num_highly_nonstationary_bands;
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}
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}
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data_dumper->DumpRaw("lc_num_stationary_bands", 1, &num_stationary_bands);
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data_dumper->DumpRaw("lc_num_highly_nonstationary_bands", 1,
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&num_highly_nonstationary_bands);
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// Use the detected number of bands to classify the overall signal
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// stationarity.
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if (num_stationary_bands > 15) {
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return SignalClassifier::SignalType::kStationary;
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} else {
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return SignalClassifier::SignalType::kNonStationary;
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}
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}
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} // namespace
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SignalClassifier::FrameExtender::FrameExtender(size_t frame_size,
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size_t extended_frame_size)
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: x_old_(extended_frame_size - frame_size, 0.f) {}
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SignalClassifier::FrameExtender::~FrameExtender() = default;
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void SignalClassifier::FrameExtender::ExtendFrame(
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rtc::ArrayView<const float> x,
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rtc::ArrayView<float> x_extended) {
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RTC_DCHECK_EQ(x_old_.size() + x.size(), x_extended.size());
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std::copy(x_old_.data(), x_old_.data() + x_old_.size(), x_extended.data());
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std::copy(x.data(), x.data() + x.size(), x_extended.data() + x_old_.size());
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std::copy(x_extended.data() + x_extended.size() - x_old_.size(),
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x_extended.data() + x_extended.size(), x_old_.data());
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}
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SignalClassifier::SignalClassifier(ApmDataDumper* data_dumper)
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: data_dumper_(data_dumper),
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down_sampler_(data_dumper_),
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noise_spectrum_estimator_(data_dumper_),
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ooura_fft_(IsSse2Available()) {
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Initialize(48000);
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}
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SignalClassifier::~SignalClassifier() {}
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void SignalClassifier::Initialize(int sample_rate_hz) {
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down_sampler_.Initialize(sample_rate_hz);
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noise_spectrum_estimator_.Initialize();
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frame_extender_.reset(new FrameExtender(80, 128));
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sample_rate_hz_ = sample_rate_hz;
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initialization_frames_left_ = 2;
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consistent_classification_counter_ = 3;
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last_signal_type_ = SignalClassifier::SignalType::kNonStationary;
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}
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SignalClassifier::SignalType SignalClassifier::Analyze(
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rtc::ArrayView<const float> signal) {
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RTC_DCHECK_EQ(signal.size(), sample_rate_hz_ / 100);
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// Compute the signal power spectrum.
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float downsampled_frame[80];
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down_sampler_.DownSample(signal, downsampled_frame);
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float extended_frame[128];
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frame_extender_->ExtendFrame(downsampled_frame, extended_frame);
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RemoveDcLevel(extended_frame);
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float signal_spectrum[65];
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PowerSpectrum(&ooura_fft_, extended_frame, signal_spectrum);
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// Classify the signal based on the estimate of the noise spectrum and the
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// signal spectrum estimate.
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const SignalType signal_type = ClassifySignal(
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signal_spectrum, noise_spectrum_estimator_.GetNoiseSpectrum(),
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data_dumper_);
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// Update the noise spectrum based on the signal spectrum.
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noise_spectrum_estimator_.Update(signal_spectrum,
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initialization_frames_left_ > 0);
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// Update the number of frames until a reliable signal spectrum is achieved.
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initialization_frames_left_ = std::max(0, initialization_frames_left_ - 1);
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if (last_signal_type_ == signal_type) {
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consistent_classification_counter_ =
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std::max(0, consistent_classification_counter_ - 1);
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} else {
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last_signal_type_ = signal_type;
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consistent_classification_counter_ = 3;
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}
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if (consistent_classification_counter_ > 0) {
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return SignalClassifier::SignalType::kNonStationary;
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}
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return signal_type;
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}
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} // namespace webrtc
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