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4ed47d0190
We put back the old noise estimator from LevelController. We add a few new unit tests. We also re-arrange the code so that it fits with how it is used in AGC2. The differences are: 1. The NoiseLevelEstimator is now fully self-contained. 2. The NoiseLevelEstimator is responsible for calling SignalClassifier and computing the signal energy. Previously the signal type and energy were used in several places. It made sense to compute the values independently of the noise calculation. 3. Re-initialization doesn't have to be done by the caller. 4. The interface is AudioFrameView instead of AudioBuffer. # Bots are green, nothing should break internal stuff NOTRY=True Bug: webrtc:7494 Change-Id: I442bdbbeb3796eb2518e96000aec9dc5a039ae6d Reviewed-on: https://webrtc-review.googlesource.com/66380 Commit-Queue: Alex Loiko <aleloi@webrtc.org> Reviewed-by: Sam Zackrisson <saza@webrtc.org> Cr-Commit-Position: refs/heads/master@{#22738}
111 lines
3.6 KiB
C++
111 lines
3.6 KiB
C++
/*
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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/noise_level_estimator.h"
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#include <math.h>
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#include <algorithm>
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#include <numeric>
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#include "common_audio/include/audio_util.h"
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#include "modules/audio_processing/logging/apm_data_dumper.h"
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namespace webrtc {
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namespace {
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constexpr int kFramesPerSecond = 100;
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float FrameEnergy(const AudioFrameView<const float>& audio) {
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float energy = 0.f;
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for (size_t k = 0; k < audio.num_channels(); ++k) {
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float channel_energy =
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std::accumulate(audio.channel(k).begin(), audio.channel(k).end(), 0.f,
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[](float a, float b) -> float { return a + b * b; });
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energy = std::max(channel_energy, energy);
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}
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return energy;
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}
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float EnergyToDbfs(float signal_energy, size_t num_samples) {
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const float rms = std::sqrt(signal_energy / num_samples);
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return FloatS16ToDbfs(rms);
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}
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} // namespace
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NoiseLevelEstimator::NoiseLevelEstimator(ApmDataDumper* data_dumper)
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: signal_classifier_(data_dumper) {
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Initialize(48000);
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}
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NoiseLevelEstimator::~NoiseLevelEstimator() {}
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void NoiseLevelEstimator::Initialize(int sample_rate_hz) {
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sample_rate_hz_ = sample_rate_hz;
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noise_energy_ = 1.f;
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first_update_ = true;
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min_noise_energy_ = sample_rate_hz * 2.f * 2.f / kFramesPerSecond;
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noise_energy_hold_counter_ = 0;
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signal_classifier_.Initialize(sample_rate_hz);
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}
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float NoiseLevelEstimator::Analyze(const AudioFrameView<const float>& frame) {
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const int rate =
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static_cast<int>(frame.samples_per_channel() * kFramesPerSecond);
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if (rate != sample_rate_hz_) {
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Initialize(rate);
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}
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const float frame_energy = FrameEnergy(frame);
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if (frame_energy <= 0.f) {
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RTC_DCHECK_GE(frame_energy, 0.f);
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return EnergyToDbfs(noise_energy_, frame.samples_per_channel());
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}
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if (first_update_) {
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// Initialize the noise energy to the frame energy.
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first_update_ = false;
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return EnergyToDbfs(
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noise_energy_ = std::max(frame_energy, min_noise_energy_),
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frame.samples_per_channel());
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}
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const SignalClassifier::SignalType signal_type =
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signal_classifier_.Analyze(frame.channel(0));
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// Update the noise estimate in a minimum statistics-type manner.
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if (signal_type == SignalClassifier::SignalType::kStationary) {
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if (frame_energy > noise_energy_) {
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// Leak the estimate upwards towards the frame energy if no recent
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// downward update.
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noise_energy_hold_counter_ = std::max(noise_energy_hold_counter_ - 1, 0);
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if (noise_energy_hold_counter_ == 0) {
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noise_energy_ = std::min(noise_energy_ * 1.01f, frame_energy);
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}
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} else {
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// Update smoothly downwards with a limited maximum update magnitude.
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noise_energy_ =
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std::max(noise_energy_ * 0.9f,
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noise_energy_ + 0.05f * (frame_energy - noise_energy_));
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noise_energy_hold_counter_ = 1000;
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}
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} else {
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// For a non-stationary signal, leak the estimate downwards in order to
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// avoid estimate locking due to incorrect signal classification.
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noise_energy_ = noise_energy_ * 0.99f;
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}
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// Ensure a minimum of the estimate.
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return EnergyToDbfs(
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noise_energy_ = std::max(noise_energy_, min_noise_energy_),
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frame.samples_per_channel());
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}
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} // namespace webrtc
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