modules/audio_processing/ns/noise_estimator.cc - src - Git at Google

 /*
  *  Copyright (c) 2019 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/ns/noise_estimator.h"

 #include <algorithm>

 #include "modules/audio_processing/ns/fast_math.h"
 #include "rtc_base/checks.h"

 namespace webrtc {

 namespace {

 // Log(i).
 constexpr std::array<float, 129> log_table = {
     0.f,       0.f,       0.f,       0.f,       0.f,       1.609438f, 1.791759f,
     1.945910f, 2.079442f, 2.197225f, 2.302585f, 2.397895f, 2.484907f, 2.564949f,
     2.639057f, 2.708050f, 2.772589f, 2.833213f, 2.890372f, 2.944439f, 2.995732f,
     3.044522f, 3.091043f, 3.135494f, 3.178054f, 3.218876f, 3.258097f, 3.295837f,
     3.332205f, 3.367296f, 3.401197f, 3.433987f, 3.465736f, 3.496507f, 3.526361f,
     3.555348f, 3.583519f, 3.610918f, 3.637586f, 3.663562f, 3.688879f, 3.713572f,
     3.737669f, 3.761200f, 3.784190f, 3.806663f, 3.828641f, 3.850147f, 3.871201f,
     3.891820f, 3.912023f, 3.931826f, 3.951244f, 3.970292f, 3.988984f, 4.007333f,
     4.025352f, 4.043051f, 4.060443f, 4.077538f, 4.094345f, 4.110874f, 4.127134f,
     4.143135f, 4.158883f, 4.174387f, 4.189655f, 4.204693f, 4.219508f, 4.234107f,
     4.248495f, 4.262680f, 4.276666f, 4.290460f, 4.304065f, 4.317488f, 4.330733f,
     4.343805f, 4.356709f, 4.369448f, 4.382027f, 4.394449f, 4.406719f, 4.418841f,
     4.430817f, 4.442651f, 4.454347f, 4.465908f, 4.477337f, 4.488636f, 4.499810f,
     4.510859f, 4.521789f, 4.532599f, 4.543295f, 4.553877f, 4.564348f, 4.574711f,
     4.584968f, 4.595119f, 4.605170f, 4.615121f, 4.624973f, 4.634729f, 4.644391f,
     4.653960f, 4.663439f, 4.672829f, 4.682131f, 4.691348f, 4.700480f, 4.709530f,
     4.718499f, 4.727388f, 4.736198f, 4.744932f, 4.753591f, 4.762174f, 4.770685f,
     4.779124f, 4.787492f, 4.795791f, 4.804021f, 4.812184f, 4.820282f, 4.828314f,
     4.836282f, 4.844187f, 4.852030f};

 }  // namespace

 NoiseEstimator::NoiseEstimator(const SuppressionParams& suppression_params)
     : suppression_params_(suppression_params) {
   noise_spectrum_.fill(0.f);
   prev_noise_spectrum_.fill(0.f);
   conservative_noise_spectrum_.fill(0.f);
   parametric_noise_spectrum_.fill(0.f);
 }

 void NoiseEstimator::PrepareAnalysis() {
   std::copy(noise_spectrum_.begin(), noise_spectrum_.end(),
             prev_noise_spectrum_.begin());
 }

 void NoiseEstimator::PreUpdate(
     int32_t num_analyzed_frames,
     rtc::ArrayView<const float, kFftSizeBy2Plus1> signal_spectrum,
     float signal_spectral_sum) {
   quantile_noise_estimator_.Estimate(signal_spectrum, noise_spectrum_);

   if (num_analyzed_frames < kShortStartupPhaseBlocks) {
     // Compute simplified noise model during startup.
     const size_t kStartBand = 5;
     float sum_log_i_log_magn = 0.f;
     float sum_log_i = 0.f;
     float sum_log_i_square = 0.f;
     float sum_log_magn = 0.f;
     for (size_t i = kStartBand; i < kFftSizeBy2Plus1; ++i) {
       float log_i = log_table[i];
       sum_log_i += log_i;
       sum_log_i_square += log_i * log_i;
       float log_signal = LogApproximation(signal_spectrum[i]);
       sum_log_magn += log_signal;
       sum_log_i_log_magn += log_i * log_signal;
     }

     // Estimate the parameter for the level of the white noise.
     constexpr float kOneByFftSizeBy2Plus1 = 1.f / kFftSizeBy2Plus1;
     white_noise_level_ += signal_spectral_sum * kOneByFftSizeBy2Plus1 *
                           suppression_params_.over_subtraction_factor;

     // Estimate pink noise parameters.
     float denom = sum_log_i_square * (kFftSizeBy2Plus1 - kStartBand) -
                   sum_log_i * sum_log_i;
     float num =
         sum_log_i_square * sum_log_magn - sum_log_i * sum_log_i_log_magn;
     RTC_DCHECK_NE(denom, 0.f);
     float pink_noise_adjustment = num / denom;

     // Constrain the estimated spectrum to be positive.
     pink_noise_adjustment = std::max(pink_noise_adjustment, 0.f);
     pink_noise_numerator_ += pink_noise_adjustment;
     num = sum_log_i * sum_log_magn -
           (kFftSizeBy2Plus1 - kStartBand) * sum_log_i_log_magn;
     RTC_DCHECK_NE(denom, 0.f);
     pink_noise_adjustment = num / denom;

     // Constrain the pink noise power to be in the interval [0, 1].
     pink_noise_adjustment = std::max(std::min(pink_noise_adjustment, 1.f), 0.f);

     pink_noise_exp_ += pink_noise_adjustment;

     const float one_by_num_analyzed_frames_plus_1 =
         1.f / (num_analyzed_frames + 1.f);

     // Calculate the frequency-independent parts of parametric noise estimate.
     float parametric_exp = 0.f;
     float parametric_num = 0.f;
     if (pink_noise_exp_ > 0.f) {
       // Use pink noise estimate.
       parametric_num = ExpApproximation(pink_noise_numerator_ *
                                         one_by_num_analyzed_frames_plus_1);
       parametric_num *= num_analyzed_frames + 1.f;
       parametric_exp = pink_noise_exp_ * one_by_num_analyzed_frames_plus_1;
     }

     constexpr float kOneByShortStartupPhaseBlocks =
         1.f / kShortStartupPhaseBlocks;
     for (size_t i = 0; i < kFftSizeBy2Plus1; ++i) {
       // Estimate the background noise using the white and pink noise
       // parameters.
       if (pink_noise_exp_ == 0.f) {
         // Use white noise estimate.
         parametric_noise_spectrum_[i] = white_noise_level_;
       } else {
         // Use pink noise estimate.
         float use_band = i < kStartBand ? kStartBand : i;
         float denom = PowApproximation(use_band, parametric_exp);
         RTC_DCHECK_NE(denom, 0.f);
         parametric_noise_spectrum_[i] = parametric_num / denom;
       }
     }

     // Weight quantile noise with modeled noise.
     for (size_t i = 0; i < kFftSizeBy2Plus1; ++i) {
       noise_spectrum_[i] *= num_analyzed_frames;
       float tmp = parametric_noise_spectrum_[i] *
                   (kShortStartupPhaseBlocks - num_analyzed_frames);
       noise_spectrum_[i] += tmp * one_by_num_analyzed_frames_plus_1;
       noise_spectrum_[i] *= kOneByShortStartupPhaseBlocks;
     }
   }
 }

 void NoiseEstimator::PostUpdate(
     rtc::ArrayView<const float> speech_probability,
     rtc::ArrayView<const float, kFftSizeBy2Plus1> signal_spectrum) {
   // Time-avg parameter for noise_spectrum update.
   constexpr float kNoiseUpdate = 0.9f;

   float gamma = kNoiseUpdate;
   for (size_t i = 0; i < kFftSizeBy2Plus1; ++i) {
     const float prob_speech = speech_probability[i];
     const float prob_non_speech = 1.f - prob_speech;

     // Temporary noise update used for speech frames if update value is less
     // than previous.
     float noise_update_tmp =
         gamma * prev_noise_spectrum_[i] +
         (1.f - gamma) * (prob_non_speech * signal_spectrum[i] +
                          prob_speech * prev_noise_spectrum_[i]);

     // Time-constant based on speech/noise_spectrum state.
     float gamma_old = gamma;

     // Increase gamma for frame likely to be seech.
     constexpr float kProbRange = .2f;
     gamma = prob_speech > kProbRange ? .99f : kNoiseUpdate;

     // Conservative noise_spectrum update.
     if (prob_speech < kProbRange) {
       conservative_noise_spectrum_[i] +=
           0.05f * (signal_spectrum[i] - conservative_noise_spectrum_[i]);
     }

     // Noise_spectrum update.
     if (gamma == gamma_old) {
       noise_spectrum_[i] = noise_update_tmp;
     } else {
       noise_spectrum_[i] =
           gamma * prev_noise_spectrum_[i] +
           (1.f - gamma) * (prob_non_speech * signal_spectrum[i] +
                            prob_speech * prev_noise_spectrum_[i]);
       // Allow for noise_spectrum update downwards: If noise_spectrum update
       // decreases the noise_spectrum, it is safe, so allow it to happen.
       noise_spectrum_[i] = std::min(noise_spectrum_[i], noise_update_tmp);
     }
   }
 }

 }  // namespace webrtc
	/*
	* Copyright (c) 2019 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/ns/noise_estimator.h"

	#include <algorithm>

	#include "modules/audio_processing/ns/fast_math.h"
	#include "rtc_base/checks.h"

	namespace webrtc {

	namespace {

	// Log(i).
	constexpr std::array<float, 129> log_table = {
	0.f, 0.f, 0.f, 0.f, 0.f, 1.609438f, 1.791759f,
	1.945910f, 2.079442f, 2.197225f, 2.302585f, 2.397895f, 2.484907f, 2.564949f,
	2.639057f, 2.708050f, 2.772589f, 2.833213f, 2.890372f, 2.944439f, 2.995732f,
	3.044522f, 3.091043f, 3.135494f, 3.178054f, 3.218876f, 3.258097f, 3.295837f,
	3.332205f, 3.367296f, 3.401197f, 3.433987f, 3.465736f, 3.496507f, 3.526361f,
	3.555348f, 3.583519f, 3.610918f, 3.637586f, 3.663562f, 3.688879f, 3.713572f,
	3.737669f, 3.761200f, 3.784190f, 3.806663f, 3.828641f, 3.850147f, 3.871201f,
	3.891820f, 3.912023f, 3.931826f, 3.951244f, 3.970292f, 3.988984f, 4.007333f,
	4.025352f, 4.043051f, 4.060443f, 4.077538f, 4.094345f, 4.110874f, 4.127134f,
	4.143135f, 4.158883f, 4.174387f, 4.189655f, 4.204693f, 4.219508f, 4.234107f,
	4.248495f, 4.262680f, 4.276666f, 4.290460f, 4.304065f, 4.317488f, 4.330733f,
	4.343805f, 4.356709f, 4.369448f, 4.382027f, 4.394449f, 4.406719f, 4.418841f,
	4.430817f, 4.442651f, 4.454347f, 4.465908f, 4.477337f, 4.488636f, 4.499810f,
	4.510859f, 4.521789f, 4.532599f, 4.543295f, 4.553877f, 4.564348f, 4.574711f,
	4.584968f, 4.595119f, 4.605170f, 4.615121f, 4.624973f, 4.634729f, 4.644391f,
	4.653960f, 4.663439f, 4.672829f, 4.682131f, 4.691348f, 4.700480f, 4.709530f,
	4.718499f, 4.727388f, 4.736198f, 4.744932f, 4.753591f, 4.762174f, 4.770685f,
	4.779124f, 4.787492f, 4.795791f, 4.804021f, 4.812184f, 4.820282f, 4.828314f,
	4.836282f, 4.844187f, 4.852030f};

	} // namespace

	NoiseEstimator::NoiseEstimator(const SuppressionParams& suppression_params)
	: suppression_params_(suppression_params) {
	noise_spectrum_.fill(0.f);
	prev_noise_spectrum_.fill(0.f);
	conservative_noise_spectrum_.fill(0.f);
	parametric_noise_spectrum_.fill(0.f);
	}

	void NoiseEstimator::PrepareAnalysis() {
	std::copy(noise_spectrum_.begin(), noise_spectrum_.end(),
	prev_noise_spectrum_.begin());
	}

	void NoiseEstimator::PreUpdate(
	int32_t num_analyzed_frames,
	rtc::ArrayView<const float, kFftSizeBy2Plus1> signal_spectrum,
	float signal_spectral_sum) {
	quantile_noise_estimator_.Estimate(signal_spectrum, noise_spectrum_);

	if (num_analyzed_frames < kShortStartupPhaseBlocks) {
	// Compute simplified noise model during startup.
	const size_t kStartBand = 5;
	float sum_log_i_log_magn = 0.f;
	float sum_log_i = 0.f;
	float sum_log_i_square = 0.f;
	float sum_log_magn = 0.f;
	for (size_t i = kStartBand; i < kFftSizeBy2Plus1; ++i) {
	float log_i = log_table[i];
	sum_log_i += log_i;
	sum_log_i_square += log_i * log_i;
	float log_signal = LogApproximation(signal_spectrum[i]);
	sum_log_magn += log_signal;
	sum_log_i_log_magn += log_i * log_signal;
	}

	// Estimate the parameter for the level of the white noise.
	constexpr float kOneByFftSizeBy2Plus1 = 1.f / kFftSizeBy2Plus1;
	white_noise_level_ += signal_spectral_sum * kOneByFftSizeBy2Plus1 *
	suppression_params_.over_subtraction_factor;

	// Estimate pink noise parameters.
	float denom = sum_log_i_square * (kFftSizeBy2Plus1 - kStartBand) -
	sum_log_i * sum_log_i;
	float num =
	sum_log_i_square * sum_log_magn - sum_log_i * sum_log_i_log_magn;
	RTC_DCHECK_NE(denom, 0.f);
	float pink_noise_adjustment = num / denom;

	// Constrain the estimated spectrum to be positive.
	pink_noise_adjustment = std::max(pink_noise_adjustment, 0.f);
	pink_noise_numerator_ += pink_noise_adjustment;
	num = sum_log_i * sum_log_magn -
	(kFftSizeBy2Plus1 - kStartBand) * sum_log_i_log_magn;
	RTC_DCHECK_NE(denom, 0.f);
	pink_noise_adjustment = num / denom;

	// Constrain the pink noise power to be in the interval [0, 1].
	pink_noise_adjustment = std::max(std::min(pink_noise_adjustment, 1.f), 0.f);

	pink_noise_exp_ += pink_noise_adjustment;

	const float one_by_num_analyzed_frames_plus_1 =
	1.f / (num_analyzed_frames + 1.f);

	// Calculate the frequency-independent parts of parametric noise estimate.
	float parametric_exp = 0.f;
	float parametric_num = 0.f;
	if (pink_noise_exp_ > 0.f) {
	// Use pink noise estimate.
	parametric_num = ExpApproximation(pink_noise_numerator_ *
	one_by_num_analyzed_frames_plus_1);
	parametric_num *= num_analyzed_frames + 1.f;
	parametric_exp = pink_noise_exp_ * one_by_num_analyzed_frames_plus_1;
	}

	constexpr float kOneByShortStartupPhaseBlocks =
	1.f / kShortStartupPhaseBlocks;
	for (size_t i = 0; i < kFftSizeBy2Plus1; ++i) {
	// Estimate the background noise using the white and pink noise
	// parameters.
	if (pink_noise_exp_ == 0.f) {
	// Use white noise estimate.
	parametric_noise_spectrum_[i] = white_noise_level_;
	} else {
	// Use pink noise estimate.
	float use_band = i < kStartBand ? kStartBand : i;
	float denom = PowApproximation(use_band, parametric_exp);
	RTC_DCHECK_NE(denom, 0.f);
	parametric_noise_spectrum_[i] = parametric_num / denom;
	}
	}

	// Weight quantile noise with modeled noise.
	for (size_t i = 0; i < kFftSizeBy2Plus1; ++i) {
	noise_spectrum_[i] *= num_analyzed_frames;
	float tmp = parametric_noise_spectrum_[i] *
	(kShortStartupPhaseBlocks - num_analyzed_frames);
	noise_spectrum_[i] += tmp * one_by_num_analyzed_frames_plus_1;
	noise_spectrum_[i] *= kOneByShortStartupPhaseBlocks;
	}
	}
	}

	void NoiseEstimator::PostUpdate(
	rtc::ArrayView<const float> speech_probability,
	rtc::ArrayView<const float, kFftSizeBy2Plus1> signal_spectrum) {
	// Time-avg parameter for noise_spectrum update.
	constexpr float kNoiseUpdate = 0.9f;

	float gamma = kNoiseUpdate;
	for (size_t i = 0; i < kFftSizeBy2Plus1; ++i) {
	const float prob_speech = speech_probability[i];
	const float prob_non_speech = 1.f - prob_speech;

	// Temporary noise update used for speech frames if update value is less
	// than previous.
	float noise_update_tmp =
	gamma * prev_noise_spectrum_[i] +
	(1.f - gamma) * (prob_non_speech * signal_spectrum[i] +
	prob_speech * prev_noise_spectrum_[i]);

	// Time-constant based on speech/noise_spectrum state.
	float gamma_old = gamma;

	// Increase gamma for frame likely to be seech.
	constexpr float kProbRange = .2f;
	gamma = prob_speech > kProbRange ? .99f : kNoiseUpdate;

	// Conservative noise_spectrum update.
	if (prob_speech < kProbRange) {
	conservative_noise_spectrum_[i] +=
	0.05f * (signal_spectrum[i] - conservative_noise_spectrum_[i]);
	}

	// Noise_spectrum update.
	if (gamma == gamma_old) {
	noise_spectrum_[i] = noise_update_tmp;
	} else {
	noise_spectrum_[i] =
	gamma * prev_noise_spectrum_[i] +
	(1.f - gamma) * (prob_non_speech * signal_spectrum[i] +
	prob_speech * prev_noise_spectrum_[i]);
	// Allow for noise_spectrum update downwards: If noise_spectrum update
	// decreases the noise_spectrum, it is safe, so allow it to happen.
	noise_spectrum_[i] = std::min(noise_spectrum_[i], noise_update_tmp);
	}
	}
	}

	} // namespace webrtc