AnotherFoxGuy/webrtc-audio-processing
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Bump to WebRTC M120 release

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Some API deprecation -- ExperimentalAgc and ExperimentalNs are gone.
We're continuing to carry iSAC even though it's gone upstream, but maybe
we'll want to drop that soon.
This commit is contained in:
Arun Raghavan
2023-12-12 10:42:58 -05:00
parent 9a202fb8c2
commit c6abf6cd3f
479 changed files with 20900 additions and 11996 deletions
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206
webrtc/modules/audio_processing/agc2/noise_level_estimator.cc
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@ -17,98 +17,156 @@
#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 energy = 0.0f;
for (int k = 0; k < audio.num_channels(); ++k) {
float channel_energy =
std::accumulate(audio.channel(k).begin(), audio.channel(k).end(), 0.f,
std::accumulate(audio.channel(k).begin(), audio.channel(k).end(), 0.0f,
[](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);
float EnergyToDbfs(float signal_energy, int num_samples) {
RTC_DCHECK_GE(signal_energy, 0.0f);
const float rms_square = signal_energy / num_samples;
constexpr float kMinDbfs = -90.30899869919436f;
if (rms_square <= 1.0f) {
return kMinDbfs;
}
return 10.0f * std::log10(rms_square) + kMinDbfs;
}
// Updates the noise floor with instant decay and slow attack. This tuning is
// specific for AGC2, so that (i) it can promptly increase the gain if the noise
// floor drops (instant decay) and (ii) in case of music or fast speech, due to
// which the noise floor can be overestimated, the gain reduction is slowed
// down.
float SmoothNoiseFloorEstimate(float current_estimate, float new_estimate) {
constexpr float kAttack = 0.5f;
if (current_estimate < new_estimate) {
// Attack phase.
return kAttack * new_estimate + (1.0f - kAttack) * current_estimate;
}
// Instant attack.
return new_estimate;
}
class NoiseFloorEstimator : public NoiseLevelEstimator {
public:
// Update the noise floor every 5 seconds.
static constexpr int kUpdatePeriodNumFrames = 500;
static_assert(kUpdatePeriodNumFrames >= 200,
"A too small value may cause noise level overestimation.");
static_assert(kUpdatePeriodNumFrames <= 1500,
"A too large value may make AGC2 slow at reacting to increased "
"noise levels.");
NoiseFloorEstimator(ApmDataDumper* data_dumper) : data_dumper_(data_dumper) {
RTC_DCHECK(data_dumper_);
// Initially assume that 48 kHz will be used. `Analyze()` will detect the
// used sample rate and call `Initialize()` again if needed.
Initialize(/*sample_rate_hz=*/48000);
}
NoiseFloorEstimator(const NoiseFloorEstimator&) = delete;
NoiseFloorEstimator& operator=(const NoiseFloorEstimator&) = delete;
~NoiseFloorEstimator() = default;
float Analyze(const AudioFrameView<const float>& frame) override {
// Detect sample rate changes.
const int sample_rate_hz =
static_cast<int>(frame.samples_per_channel() * kFramesPerSecond);
if (sample_rate_hz != sample_rate_hz_) {
Initialize(sample_rate_hz);
}
const float frame_energy = FrameEnergy(frame);
if (frame_energy <= min_noise_energy_) {
// Ignore frames when muted or below the minimum measurable energy.
if (data_dumper_)
data_dumper_->DumpRaw("agc2_noise_floor_estimator_preliminary_level",
noise_energy_);
return EnergyToDbfs(noise_energy_,
static_cast<int>(frame.samples_per_channel()));
}
if (preliminary_noise_energy_set_) {
preliminary_noise_energy_ =
std::min(preliminary_noise_energy_, frame_energy);
} else {
preliminary_noise_energy_ = frame_energy;
preliminary_noise_energy_set_ = true;
}
if (data_dumper_)
data_dumper_->DumpRaw("agc2_noise_floor_estimator_preliminary_level",
preliminary_noise_energy_);
if (counter_ == 0) {
// Full period observed.
first_period_ = false;
// Update the estimated noise floor energy with the preliminary
// estimation.
noise_energy_ = SmoothNoiseFloorEstimate(
/*current_estimate=*/noise_energy_,
/*new_estimate=*/preliminary_noise_energy_);
// Reset for a new observation period.
counter_ = kUpdatePeriodNumFrames;
preliminary_noise_energy_set_ = false;
} else if (first_period_) {
// While analyzing the signal during the initial period, continuously
// update the estimated noise energy, which is monotonic.
noise_energy_ = preliminary_noise_energy_;
counter_--;
} else {
// During the observation period it's only allowed to lower the energy.
noise_energy_ = std::min(noise_energy_, preliminary_noise_energy_);
counter_--;
}
float noise_rms_dbfs = EnergyToDbfs(
noise_energy_, static_cast<int>(frame.samples_per_channel()));
if (data_dumper_)
data_dumper_->DumpRaw("agc2_noise_rms_dbfs", noise_rms_dbfs);
return noise_rms_dbfs;
}
private:
void Initialize(int sample_rate_hz) {
sample_rate_hz_ = sample_rate_hz;
first_period_ = true;
preliminary_noise_energy_set_ = false;
// Initialize the minimum noise energy to -84 dBFS.
min_noise_energy_ = sample_rate_hz * 2.0f * 2.0f / kFramesPerSecond;
preliminary_noise_energy_ = min_noise_energy_;
noise_energy_ = min_noise_energy_;
counter_ = kUpdatePeriodNumFrames;
}
ApmDataDumper* const data_dumper_;
int sample_rate_hz_;
float min_noise_energy_;
bool first_period_;
bool preliminary_noise_energy_set_;
float preliminary_noise_energy_;
float noise_energy_;
int counter_;
};
} // 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());
std::unique_ptr<NoiseLevelEstimator> CreateNoiseFloorEstimator(
ApmDataDumper* data_dumper) {
return std::make_unique<NoiseFloorEstimator>(data_dumper);
}
} // namespace webrtc