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/*
* Copyright (c) 2016 The WebRTC project authors. All Rights Reserved.
*
* Use of this source code is governed by a BSD-style license
* that can be found in the LICENSE file in the root of the source
* tree. An additional intellectual property rights grant can be found
* in the file PATENTS. All contributing project authors may
* be found in the AUTHORS file in the root of the source tree.
*/
#include "modules/audio_processing/agc2/noise_level_estimator.h"
#include <stddef.h>
#include <algorithm>
#include <cmath>
#include <numeric>
#include "api/array_view.h"
#include "common_audio/include/audio_util.h"
#include "modules/audio_processing/logging/apm_data_dumper.h"
#include "rtc_base/checks.h"
namespace webrtc {
namespace {
constexpr int kFramesPerSecond = 100;
float FrameEnergy(const AudioFrameView<const float>& audio) {
float energy = 0.f;
for (size_t k = 0; k < audio.num_channels(); ++k) {
float channel_energy =
std::accumulate(audio.channel(k).begin(), audio.channel(k).end(), 0.f,
[](float a, float b) -> float { return a + b * b; });
energy = std::max(channel_energy, energy);
}
return energy;
}
float EnergyToDbfs(float signal_energy, size_t num_samples) {
const float rms = std::sqrt(signal_energy / num_samples);
return FloatS16ToDbfs(rms);
}
} // namespace
NoiseLevelEstimator::NoiseLevelEstimator(ApmDataDumper* data_dumper)
: signal_classifier_(data_dumper) {
Initialize(48000);
}
NoiseLevelEstimator::~NoiseLevelEstimator() {}
void NoiseLevelEstimator::Initialize(int sample_rate_hz) {
sample_rate_hz_ = sample_rate_hz;
noise_energy_ = 1.f;
first_update_ = true;
min_noise_energy_ = sample_rate_hz * 2.f * 2.f / kFramesPerSecond;
noise_energy_hold_counter_ = 0;
signal_classifier_.Initialize(sample_rate_hz);
}
float NoiseLevelEstimator::Analyze(const AudioFrameView<const float>& frame) {
const int rate =
static_cast<int>(frame.samples_per_channel() * kFramesPerSecond);
if (rate != sample_rate_hz_) {
Initialize(rate);
}
const float frame_energy = FrameEnergy(frame);
if (frame_energy <= 0.f) {
RTC_DCHECK_GE(frame_energy, 0.f);
return EnergyToDbfs(noise_energy_, frame.samples_per_channel());
}
if (first_update_) {
// Initialize the noise energy to the frame energy.
first_update_ = false;
return EnergyToDbfs(
noise_energy_ = std::max(frame_energy, min_noise_energy_),
frame.samples_per_channel());
}
const SignalClassifier::SignalType signal_type =
signal_classifier_.Analyze(frame.channel(0));
// Update the noise estimate in a minimum statistics-type manner.
if (signal_type == SignalClassifier::SignalType::kStationary) {
if (frame_energy > noise_energy_) {
// Leak the estimate upwards towards the frame energy if no recent
// downward update.
noise_energy_hold_counter_ = std::max(noise_energy_hold_counter_ - 1, 0);
if (noise_energy_hold_counter_ == 0) {
noise_energy_ = std::min(noise_energy_ * 1.01f, frame_energy);
}
} else {
// Update smoothly downwards with a limited maximum update magnitude.
noise_energy_ =
std::max(noise_energy_ * 0.9f,
noise_energy_ + 0.05f * (frame_energy - noise_energy_));
noise_energy_hold_counter_ = 1000;
}
} else {
// For a non-stationary signal, leak the estimate downwards in order to
// avoid estimate locking due to incorrect signal classification.
noise_energy_ = noise_energy_ * 0.99f;
}
// Ensure a minimum of the estimate.
return EnergyToDbfs(
noise_energy_ = std::max(noise_energy_, min_noise_energy_),
frame.samples_per_channel());
}
} // namespace webrtc
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