tor-browser

noise_level_estimator.cc (6135B)

---

/*
*  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 <algorithm>
#include <cmath>
#include <cstddef>
#include <memory>
#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() override = 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