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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/audio/audio_view.h"
#include "modules/audio_processing/logging/apm_data_dumper.h"
#include "rtc_base/checks.h"
namespace webrtc {
namespace {
constexpr int kFramesPerSecond = 100;
float FrameEnergy(DeinterleavedView<const float> audio) {
float energy = 0.0f;
for (size_t k = 0; k < audio.num_channels(); ++k) {
MonoView<const float> ch = audio[k];
float channel_energy =
std::accumulate(ch.begin(), ch.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, 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(DeinterleavedView<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
std::unique_ptr<NoiseLevelEstimator> CreateNoiseFloorEstimator(
ApmDataDumper* data_dumper) {
return std::make_unique<NoiseFloorEstimator>(data_dumper);
}
} // namespace webrtc
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