bcec8b0b21
This is from the upstream library commit id 3326535126e435f1ba647885ce43a8f0f3d317eb, corresponding to Chromium 88.0.4290.1.
115 lines
3.7 KiB
C++
115 lines
3.7 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 <stddef.h>
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#include <algorithm>
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#include <cmath>
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#include <numeric>
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#include "api/array_view.h"
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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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#include "rtc_base/checks.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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