modules/audio_processing/agc2/noise_level_estimator.cc

/*
 *  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/agc2/signal_classifier.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 = 0.0f;
  for (size_t k = 0; k < audio.num_channels(); ++k) {
    float channel_energy =
        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);
}

class NoiseLevelEstimatorImpl : public NoiseLevelEstimator {
 public:
  NoiseLevelEstimatorImpl(ApmDataDumper* data_dumper)
      : data_dumper_(data_dumper), signal_classifier_(data_dumper) {
    Initialize(48000);
  }
  NoiseLevelEstimatorImpl(const NoiseLevelEstimatorImpl&) = delete;
  NoiseLevelEstimatorImpl& operator=(const NoiseLevelEstimatorImpl&) = delete;
  ~NoiseLevelEstimatorImpl() = default;

  float Analyze(const AudioFrameView<const float>& frame) {
    data_dumper_->DumpRaw("agc2_noise_level_estimator_hold_counter",
                          noise_energy_hold_counter_);
    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 <= 0.f) {
      RTC_DCHECK_GE(frame_energy, 0.f);
      data_dumper_->DumpRaw("agc2_noise_level_estimator_signal_type", -1);
      return EnergyToDbfs(noise_energy_, frame.samples_per_channel());
    }

    if (first_update_) {
      // Initialize the noise energy to the frame energy.
      first_update_ = false;
      data_dumper_->DumpRaw("agc2_noise_level_estimator_signal_type", -1);
      noise_energy_ = std::max(frame_energy, min_noise_energy_);
      return EnergyToDbfs(noise_energy_, frame.samples_per_channel());
    }

    const SignalClassifier::SignalType signal_type =
        signal_classifier_.Analyze(frame.channel(0));
    data_dumper_->DumpRaw("agc2_noise_level_estimator_signal_type",
                          static_cast<int>(signal_type));

    // 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) {
          constexpr float kMaxNoiseEnergyFactor = 1.01f;
          noise_energy_ =
              std::min(noise_energy_ * kMaxNoiseEnergyFactor, frame_energy);
        }
      } else {
        // Update smoothly downwards with a limited maximum update magnitude.
        constexpr float kMinNoiseEnergyFactor = 0.9f;
        constexpr float kNoiseEnergyDeltaFactor = 0.05f;
        noise_energy_ =
            std::max(noise_energy_ * kMinNoiseEnergyFactor,
                     noise_energy_ - kNoiseEnergyDeltaFactor *
                                         (noise_energy_ - frame_energy));
        // Prevent an energy increase for the next 10 seconds.
        constexpr int kNumFramesToEnergyIncreaseAllowed = 1000;
        noise_energy_hold_counter_ = kNumFramesToEnergyIncreaseAllowed;
      }
    } else {
      // TODO(bugs.webrtc.org/7494): Remove to not forget the estimated level.
      // 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.
    noise_energy_ = std::max(noise_energy_, min_noise_energy_);
    return EnergyToDbfs(noise_energy_, frame.samples_per_channel());
  }

 private:
  void Initialize(int sample_rate_hz) {
    sample_rate_hz_ = sample_rate_hz;
    noise_energy_ = 1.0f;
    first_update_ = true;
    min_noise_energy_ = sample_rate_hz * 2.0f * 2.0f / kFramesPerSecond;
    noise_energy_hold_counter_ = 0;
    signal_classifier_.Initialize(sample_rate_hz);
  }

  ApmDataDumper* const data_dumper_;
  int sample_rate_hz_;
  float min_noise_energy_;
  bool first_update_;
  float noise_energy_;
  int noise_energy_hold_counter_;
  SignalClassifier signal_classifier_;
};

}  // namespace

std::unique_ptr<NoiseLevelEstimator> CreateNoiseLevelEstimator(
    ApmDataDumper* data_dumper) {
  return std::make_unique<NoiseLevelEstimatorImpl>(data_dumper);
}

}  // namespace webrtc