webrtc/modules/audio_processing/intelligibility/intelligibility_enhancer.cc - platform/external/webrtc - Git at Google

 /*
  *  Copyright (c) 2014 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.
  */

 //
 //  Implements core class for intelligibility enhancer.
 //
 //  Details of the model and algorithm can be found in the original paper:
 //  http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6882788
 //

 #include "webrtc/modules/audio_processing/intelligibility/intelligibility_enhancer.h"

 #include <math.h>
 #include <stdlib.h>

 #include <algorithm>
 #include <numeric>

 #include "webrtc/base/checks.h"
 #include "webrtc/common_audio/vad/include/webrtc_vad.h"
 #include "webrtc/common_audio/window_generator.h"

 namespace webrtc {

 namespace {

 const int kWindowSizeMs = 2;
 const int kChunkSizeMs = 10;  // Size provided by APM.
 const float kClipFreq = 200.0f;
 const float kConfigRho = 0.02f;  // Default production and interpretation SNR.
 const float kKbdAlpha = 1.5f;
 const float kLambdaBot = -1.0f;      // Extreme values in bisection
 const float kLambdaTop = -10e-18f;  // search for lamda.

 }  // namespace

 using std::complex;
 using std::max;
 using std::min;
 using VarianceType = intelligibility::VarianceArray::StepType;

 IntelligibilityEnhancer::TransformCallback::TransformCallback(
     IntelligibilityEnhancer* parent,
     IntelligibilityEnhancer::AudioSource source)
     : parent_(parent), source_(source) {
 }

 void IntelligibilityEnhancer::TransformCallback::ProcessAudioBlock(
     const complex<float>* const* in_block,
     int in_channels,
     int frames,
     int /* out_channels */,
     complex<float>* const* out_block) {
   DCHECK_EQ(parent_->freqs_, frames);
   for (int i = 0; i < in_channels; ++i) {
     parent_->DispatchAudio(source_, in_block[i], out_block[i]);
   }
 }

 IntelligibilityEnhancer::IntelligibilityEnhancer(int erb_resolution,
                                                  int sample_rate_hz,
                                                  int channels,
                                                  int cv_type,
                                                  float cv_alpha,
                                                  int cv_win,
                                                  int analysis_rate,
                                                  int variance_rate,
                                                  float gain_limit)
     : freqs_(RealFourier::ComplexLength(
           RealFourier::FftOrder(sample_rate_hz * kWindowSizeMs / 1000))),
       window_size_(1 << RealFourier::FftOrder(freqs_)),
       chunk_length_(sample_rate_hz * kChunkSizeMs / 1000),
       bank_size_(GetBankSize(sample_rate_hz, erb_resolution)),
       sample_rate_hz_(sample_rate_hz),
       erb_resolution_(erb_resolution),
       channels_(channels),
       analysis_rate_(analysis_rate),
       variance_rate_(variance_rate),
       clear_variance_(freqs_,
                       static_cast<VarianceType>(cv_type),
                       cv_win,
                       cv_alpha),
       noise_variance_(freqs_, VarianceType::kStepInfinite, 475, 0.01f),
       filtered_clear_var_(new float[bank_size_]),
       filtered_noise_var_(new float[bank_size_]),
       filter_bank_(bank_size_),
       center_freqs_(new float[bank_size_]),
       rho_(new float[bank_size_]),
       gains_eq_(new float[bank_size_]),
       gain_applier_(freqs_, gain_limit),
       temp_out_buffer_(nullptr),
       input_audio_(new float* [channels]),
       kbd_window_(new float[window_size_]),
       render_callback_(this, AudioSource::kRenderStream),
       capture_callback_(this, AudioSource::kCaptureStream),
       block_count_(0),
       analysis_step_(0),
       vad_high_(WebRtcVad_Create()),
       vad_low_(WebRtcVad_Create()),
       vad_tmp_buffer_(new int16_t[chunk_length_]) {
   DCHECK_LE(kConfigRho, 1.0f);

   CreateErbBank();

   WebRtcVad_Init(vad_high_);
   WebRtcVad_set_mode(vad_high_, 0);  // High likelihood of speech.
   WebRtcVad_Init(vad_low_);
   WebRtcVad_set_mode(vad_low_, 3);  // Low likelihood of speech.

   temp_out_buffer_ = static_cast<float**>(
       malloc(sizeof(*temp_out_buffer_) * channels_ +
              sizeof(**temp_out_buffer_) * chunk_length_ * channels_));
   for (int i = 0; i < channels_; ++i) {
     temp_out_buffer_[i] =
         reinterpret_cast<float*>(temp_out_buffer_ + channels_) +
         chunk_length_ * i;
   }

   // Assumes all rho equal.
   for (int i = 0; i < bank_size_; ++i) {
     rho_[i] = kConfigRho * kConfigRho;
   }

   float freqs_khz = kClipFreq / 1000.0f;
   int erb_index = static_cast<int>(ceilf(
       11.17f * logf((freqs_khz + 0.312f) / (freqs_khz + 14.6575f)) + 43.0f));
   start_freq_ = std::max(1, erb_index * erb_resolution);

   WindowGenerator::KaiserBesselDerived(kKbdAlpha, window_size_,
                                        kbd_window_.get());
   render_mangler_.reset(new LappedTransform(
       channels_, channels_, chunk_length_, kbd_window_.get(), window_size_,
       window_size_ / 2, &render_callback_));
   capture_mangler_.reset(new LappedTransform(
       channels_, channels_, chunk_length_, kbd_window_.get(), window_size_,
       window_size_ / 2, &capture_callback_));
 }

 IntelligibilityEnhancer::~IntelligibilityEnhancer() {
   WebRtcVad_Free(vad_low_);
   WebRtcVad_Free(vad_high_);
   free(temp_out_buffer_);
 }

 void IntelligibilityEnhancer::ProcessRenderAudio(float* const* audio) {
   for (int i = 0; i < chunk_length_; ++i) {
     vad_tmp_buffer_[i] = (int16_t)audio[0][i];
   }
   has_voice_low_ = WebRtcVad_Process(vad_low_, sample_rate_hz_,
                                      vad_tmp_buffer_.get(), chunk_length_) == 1;

   // Process and enhance chunk of |audio|
   render_mangler_->ProcessChunk(audio, temp_out_buffer_);

   for (int i = 0; i < channels_; ++i) {
     memcpy(audio[i], temp_out_buffer_[i],
            chunk_length_ * sizeof(**temp_out_buffer_));
   }
 }

 void IntelligibilityEnhancer::ProcessCaptureAudio(float* const* audio) {
   for (int i = 0; i < chunk_length_; ++i) {
     vad_tmp_buffer_[i] = (int16_t)audio[0][i];
   }
   // TODO(bercic): The VAD was always detecting voice in the noise stream,
   // no matter what the aggressiveness, so it was temporarily disabled here.

   #if 0
     if (WebRtcVad_Process(vad_high_, sample_rate_hz_, vad_tmp_buffer_.get(),
       chunk_length_) == 1) {
       printf("capture HAS speech\n");
       return;
     }
     printf("capture NO speech\n");
   #endif

   capture_mangler_->ProcessChunk(audio, temp_out_buffer_);
 }

 void IntelligibilityEnhancer::DispatchAudio(
     IntelligibilityEnhancer::AudioSource source,
     const complex<float>* in_block,
     complex<float>* out_block) {
   switch (source) {
     case kRenderStream:
       ProcessClearBlock(in_block, out_block);
       break;
     case kCaptureStream:
       ProcessNoiseBlock(in_block, out_block);
       break;
   }
 }

 void IntelligibilityEnhancer::ProcessClearBlock(const complex<float>* in_block,
                                                 complex<float>* out_block) {
   if (block_count_ < 2) {
     memset(out_block, 0, freqs_ * sizeof(*out_block));
     ++block_count_;
     return;
   }

   // For now, always assumes enhancement is necessary.
   // TODO(ekmeyerson): Change to only enhance if necessary,
   // based on experiments with different cutoffs.
   if (has_voice_low_ || true) {
     clear_variance_.Step(in_block, false);
     const float power_target = std::accumulate(
         clear_variance_.variance(), clear_variance_.variance() + freqs_, 0.0f);

     if (block_count_ % analysis_rate_ == analysis_rate_ - 1) {
       AnalyzeClearBlock(power_target);
       ++analysis_step_;
       if (analysis_step_ == variance_rate_) {
         analysis_step_ = 0;
         clear_variance_.Clear();
         noise_variance_.Clear();
       }
     }
     ++block_count_;
   }

   /* efidata(n,:) = sqrt(b(n)) * fidata(n,:) */
   gain_applier_.Apply(in_block, out_block);
 }

 void IntelligibilityEnhancer::AnalyzeClearBlock(float power_target) {
   FilterVariance(clear_variance_.variance(), filtered_clear_var_.get());
   FilterVariance(noise_variance_.variance(), filtered_noise_var_.get());

   SolveForGainsGivenLambda(kLambdaTop, start_freq_, gains_eq_.get());
   const float power_top =
       DotProduct(gains_eq_.get(), filtered_clear_var_.get(), bank_size_);
   SolveForGainsGivenLambda(kLambdaBot, start_freq_, gains_eq_.get());
   const float power_bot =
       DotProduct(gains_eq_.get(), filtered_clear_var_.get(), bank_size_);
   if (power_target >= power_bot && power_target <= power_top) {
     SolveForLambda(power_target, power_bot, power_top);
     UpdateErbGains();
   }  // Else experiencing variance underflow, so do nothing.
 }

 void IntelligibilityEnhancer::SolveForLambda(float power_target,
                                              float power_bot,
                                              float power_top) {
   const float kConvergeThresh = 0.001f;  // TODO(ekmeyerson): Find best values
   const int kMaxIters = 100;             // for these, based on experiments.

   const float reciprocal_power_target = 1.f / power_target;
   float lambda_bot = kLambdaBot;
   float lambda_top = kLambdaTop;
   float power_ratio = 2.0f;  // Ratio of achieved power to target power.
   int iters = 0;
   while (std::fabs(power_ratio - 1.0f) > kConvergeThresh &&
          iters <= kMaxIters) {
     const float lambda = lambda_bot + (lambda_top - lambda_bot) / 2.0f;
     SolveForGainsGivenLambda(lambda, start_freq_, gains_eq_.get());
     const float power =
         DotProduct(gains_eq_.get(), filtered_clear_var_.get(), bank_size_);
     if (power < power_target) {
       lambda_bot = lambda;
     } else {
       lambda_top = lambda;
     }
     power_ratio = std::fabs(power * reciprocal_power_target);
     ++iters;
   }
 }

 void IntelligibilityEnhancer::UpdateErbGains() {
   // (ERB gain) = filterbank' * (freq gain)
   float* gains = gain_applier_.target();
   for (int i = 0; i < freqs_; ++i) {
     gains[i] = 0.0f;
     for (int j = 0; j < bank_size_; ++j) {
       gains[i] = fmaf(filter_bank_[j][i], gains_eq_[j], gains[i]);
     }
   }
 }

 void IntelligibilityEnhancer::ProcessNoiseBlock(const complex<float>* in_block,
                                                 complex<float>* /*out_block*/) {
   noise_variance_.Step(in_block);
 }

 int IntelligibilityEnhancer::GetBankSize(int sample_rate, int erb_resolution) {
   float freq_limit = sample_rate / 2000.0f;
   int erb_scale = ceilf(
       11.17f * logf((freq_limit + 0.312f) / (freq_limit + 14.6575f)) + 43.0f);
   return erb_scale * erb_resolution;
 }

 void IntelligibilityEnhancer::CreateErbBank() {
   int lf = 1, rf = 4;

   for (int i = 0; i < bank_size_; ++i) {
     float abs_temp = fabsf((i + 1.0f) / static_cast<float>(erb_resolution_));
     center_freqs_[i] = 676170.4f / (47.06538f - expf(0.08950404f * abs_temp));
     center_freqs_[i] -= 14678.49f;
   }
   float last_center_freq = center_freqs_[bank_size_ - 1];
   for (int i = 0; i < bank_size_; ++i) {
     center_freqs_[i] *= 0.5f * sample_rate_hz_ / last_center_freq;
   }

   for (int i = 0; i < bank_size_; ++i) {
     filter_bank_[i].resize(freqs_);
   }

   for (int i = 1; i <= bank_size_; ++i) {
     int lll, ll, rr, rrr;
     lll = round(center_freqs_[max(1, i - lf) - 1] * freqs_ /
                 (0.5f * sample_rate_hz_));
     ll =
         round(center_freqs_[max(1, i) - 1] * freqs_ / (0.5f * sample_rate_hz_));
     lll = min(freqs_, max(lll, 1)) - 1;
     ll = min(freqs_, max(ll, 1)) - 1;

     rrr = round(center_freqs_[min(bank_size_, i + rf) - 1] * freqs_ /
                 (0.5f * sample_rate_hz_));
     rr = round(center_freqs_[min(bank_size_, i + 1) - 1] * freqs_ /
                (0.5f * sample_rate_hz_));
     rrr = min(freqs_, max(rrr, 1)) - 1;
     rr = min(freqs_, max(rr, 1)) - 1;

     float step, element;

     step = 1.0f / (ll - lll);
     element = 0.0f;
     for (int j = lll; j <= ll; ++j) {
       filter_bank_[i - 1][j] = element;
       element += step;
     }
     step = 1.0f / (rrr - rr);
     element = 1.0f;
     for (int j = rr; j <= rrr; ++j) {
       filter_bank_[i - 1][j] = element;
       element -= step;
     }
     for (int j = ll; j <= rr; ++j) {
       filter_bank_[i - 1][j] = 1.0f;
     }
   }

   float sum;
   for (int i = 0; i < freqs_; ++i) {
     sum = 0.0f;
     for (int j = 0; j < bank_size_; ++j) {
       sum += filter_bank_[j][i];
     }
     for (int j = 0; j < bank_size_; ++j) {
       filter_bank_[j][i] /= sum;
     }
   }
 }

 void IntelligibilityEnhancer::SolveForGainsGivenLambda(float lambda,
                                                        int start_freq,
                                                        float* sols) {
   bool quadratic = (kConfigRho < 1.0f);
   const float* var_x0 = filtered_clear_var_.get();
   const float* var_n0 = filtered_noise_var_.get();

   for (int n = 0; n < start_freq; ++n) {
     sols[n] = 1.0f;
   }

   // Analytic solution for optimal gains. See paper for derivation.
   for (int n = start_freq - 1; n < bank_size_; ++n) {
     float alpha0, beta0, gamma0;
     gamma0 = 0.5f * rho_[n] * var_x0[n] * var_n0[n] +
              lambda * var_x0[n] * var_n0[n] * var_n0[n];
     beta0 = lambda * var_x0[n] * (2 - rho_[n]) * var_x0[n] * var_n0[n];
     if (quadratic) {
       alpha0 = lambda * var_x0[n] * (1 - rho_[n]) * var_x0[n] * var_x0[n];
       sols[n] =
           (-beta0 - sqrtf(beta0 * beta0 - 4 * alpha0 * gamma0)) / (2 * alpha0);
     } else {
       sols[n] = -gamma0 / beta0;
     }
     sols[n] = fmax(0, sols[n]);
   }
 }

 void IntelligibilityEnhancer::FilterVariance(const float* var, float* result) {
   DCHECK_GT(freqs_, 0);
   for (int i = 0; i < bank_size_; ++i) {
     result[i] = DotProduct(&filter_bank_[i][0], var, freqs_);
   }
 }

 float IntelligibilityEnhancer::DotProduct(const float* a,
                                           const float* b,
                                           int length) {
   float ret = 0.0f;

   for (int i = 0; i < length; ++i) {
     ret = fmaf(a[i], b[i], ret);
   }
   return ret;
 }

 }  // namespace webrtc
	/*
	* Copyright (c) 2014 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.
	*/

	//
	// Implements core class for intelligibility enhancer.
	//
	// Details of the model and algorithm can be found in the original paper:
	// http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6882788
	//

	#include "webrtc/modules/audio_processing/intelligibility/intelligibility_enhancer.h"

	#include <math.h>
	#include <stdlib.h>

	#include <algorithm>
	#include <numeric>

	#include "webrtc/base/checks.h"
	#include "webrtc/common_audio/vad/include/webrtc_vad.h"
	#include "webrtc/common_audio/window_generator.h"

	namespace webrtc {

	namespace {

	const int kWindowSizeMs = 2;
	const int kChunkSizeMs = 10; // Size provided by APM.
	const float kClipFreq = 200.0f;
	const float kConfigRho = 0.02f; // Default production and interpretation SNR.
	const float kKbdAlpha = 1.5f;
	const float kLambdaBot = -1.0f; // Extreme values in bisection
	const float kLambdaTop = -10e-18f; // search for lamda.

	} // namespace

	using std::complex;
	using std::max;
	using std::min;
	using VarianceType = intelligibility::VarianceArray::StepType;

	IntelligibilityEnhancer::TransformCallback::TransformCallback(
	IntelligibilityEnhancer* parent,
	IntelligibilityEnhancer::AudioSource source)
	: parent_(parent), source_(source) {
	}

	void IntelligibilityEnhancer::TransformCallback::ProcessAudioBlock(
	const complex<float>* const* in_block,
	int in_channels,
	int frames,
	int /* out_channels */,
	complex<float>* const* out_block) {
	DCHECK_EQ(parent_->freqs_, frames);
	for (int i = 0; i < in_channels; ++i) {
	parent_->DispatchAudio(source_, in_block[i], out_block[i]);
	}
	}

	IntelligibilityEnhancer::IntelligibilityEnhancer(int erb_resolution,
	int sample_rate_hz,
	int channels,
	int cv_type,
	float cv_alpha,
	int cv_win,
	int analysis_rate,
	int variance_rate,
	float gain_limit)
	: freqs_(RealFourier::ComplexLength(
	RealFourier::FftOrder(sample_rate_hz * kWindowSizeMs / 1000))),
	window_size_(1 << RealFourier::FftOrder(freqs_)),
	chunk_length_(sample_rate_hz * kChunkSizeMs / 1000),
	bank_size_(GetBankSize(sample_rate_hz, erb_resolution)),
	sample_rate_hz_(sample_rate_hz),
	erb_resolution_(erb_resolution),
	channels_(channels),
	analysis_rate_(analysis_rate),
	variance_rate_(variance_rate),
	clear_variance_(freqs_,
	static_cast<VarianceType>(cv_type),
	cv_win,
	cv_alpha),
	noise_variance_(freqs_, VarianceType::kStepInfinite, 475, 0.01f),
	filtered_clear_var_(new float[bank_size_]),
	filtered_noise_var_(new float[bank_size_]),
	filter_bank_(bank_size_),
	center_freqs_(new float[bank_size_]),
	rho_(new float[bank_size_]),
	gains_eq_(new float[bank_size_]),
	gain_applier_(freqs_, gain_limit),
	temp_out_buffer_(nullptr),
	input_audio_(new float* [channels]),
	kbd_window_(new float[window_size_]),
	render_callback_(this, AudioSource::kRenderStream),
	capture_callback_(this, AudioSource::kCaptureStream),
	block_count_(0),
	analysis_step_(0),
	vad_high_(WebRtcVad_Create()),
	vad_low_(WebRtcVad_Create()),
	vad_tmp_buffer_(new int16_t[chunk_length_]) {
	DCHECK_LE(kConfigRho, 1.0f);

	CreateErbBank();

	WebRtcVad_Init(vad_high_);
	WebRtcVad_set_mode(vad_high_, 0); // High likelihood of speech.
	WebRtcVad_Init(vad_low_);
	WebRtcVad_set_mode(vad_low_, 3); // Low likelihood of speech.

	temp_out_buffer_ = static_cast<float**>(
	malloc(sizeof(temp_out_buffer_) channels_ +
	sizeof(*temp_out_buffer_) chunk_length_ * channels_));
	for (int i = 0; i < channels_; ++i) {
	temp_out_buffer_[i] =
	reinterpret_cast<float*>(temp_out_buffer_ + channels_) +
	chunk_length_ * i;
	}

	// Assumes all rho equal.
	for (int i = 0; i < bank_size_; ++i) {
	rho_[i] = kConfigRho * kConfigRho;
	}

	float freqs_khz = kClipFreq / 1000.0f;
	int erb_index = static_cast<int>(ceilf(
	11.17f * logf((freqs_khz + 0.312f) / (freqs_khz + 14.6575f)) + 43.0f));
	start_freq_ = std::max(1, erb_index * erb_resolution);

	WindowGenerator::KaiserBesselDerived(kKbdAlpha, window_size_,
	kbd_window_.get());
	render_mangler_.reset(new LappedTransform(
	channels_, channels_, chunk_length_, kbd_window_.get(), window_size_,
	window_size_ / 2, &render_callback_));
	capture_mangler_.reset(new LappedTransform(
	channels_, channels_, chunk_length_, kbd_window_.get(), window_size_,
	window_size_ / 2, &capture_callback_));
	}

	IntelligibilityEnhancer::~IntelligibilityEnhancer() {
	WebRtcVad_Free(vad_low_);
	WebRtcVad_Free(vad_high_);
	free(temp_out_buffer_);
	}

	void IntelligibilityEnhancer::ProcessRenderAudio(float* const* audio) {
	for (int i = 0; i < chunk_length_; ++i) {
	vad_tmp_buffer_[i] = (int16_t)audio[0][i];
	}
	has_voice_low_ = WebRtcVad_Process(vad_low_, sample_rate_hz_,
	vad_tmp_buffer_.get(), chunk_length_) == 1;

	// Process and enhance chunk of \|audio\|
	render_mangler_->ProcessChunk(audio, temp_out_buffer_);

	for (int i = 0; i < channels_; ++i) {
	memcpy(audio[i], temp_out_buffer_[i],
	chunk_length_ * sizeof(**temp_out_buffer_));
	}
	}

	void IntelligibilityEnhancer::ProcessCaptureAudio(float* const* audio) {
	for (int i = 0; i < chunk_length_; ++i) {
	vad_tmp_buffer_[i] = (int16_t)audio[0][i];
	}
	// TODO(bercic): The VAD was always detecting voice in the noise stream,
	// no matter what the aggressiveness, so it was temporarily disabled here.

	#if 0
	if (WebRtcVad_Process(vad_high_, sample_rate_hz_, vad_tmp_buffer_.get(),
	chunk_length_) == 1) {
	printf("capture HAS speech\n");
	return;
	}
	printf("capture NO speech\n");
	#endif

	capture_mangler_->ProcessChunk(audio, temp_out_buffer_);
	}

	void IntelligibilityEnhancer::DispatchAudio(
	IntelligibilityEnhancer::AudioSource source,
	const complex<float>* in_block,
	complex<float>* out_block) {
	switch (source) {
	case kRenderStream:
	ProcessClearBlock(in_block, out_block);
	break;
	case kCaptureStream:
	ProcessNoiseBlock(in_block, out_block);
	break;
	}
	}

	void IntelligibilityEnhancer::ProcessClearBlock(const complex<float>* in_block,
	complex<float>* out_block) {
	if (block_count_ < 2) {
	memset(out_block, 0, freqs_ * sizeof(*out_block));
	++block_count_;
	return;
	}

	// For now, always assumes enhancement is necessary.
	// TODO(ekmeyerson): Change to only enhance if necessary,
	// based on experiments with different cutoffs.
	if (has_voice_low_ \|\| true) {
	clear_variance_.Step(in_block, false);
	const float power_target = std::accumulate(
	clear_variance_.variance(), clear_variance_.variance() + freqs_, 0.0f);

	if (block_count_ % analysis_rate_ == analysis_rate_ - 1) {
	AnalyzeClearBlock(power_target);
	++analysis_step_;
	if (analysis_step_ == variance_rate_) {
	analysis_step_ = 0;
	clear_variance_.Clear();
	noise_variance_.Clear();
	}
	}
	++block_count_;
	}

	/* efidata(n,:) = sqrt(b(n)) * fidata(n,:) */
	gain_applier_.Apply(in_block, out_block);
	}

	void IntelligibilityEnhancer::AnalyzeClearBlock(float power_target) {
	FilterVariance(clear_variance_.variance(), filtered_clear_var_.get());
	FilterVariance(noise_variance_.variance(), filtered_noise_var_.get());

	SolveForGainsGivenLambda(kLambdaTop, start_freq_, gains_eq_.get());
	const float power_top =
	DotProduct(gains_eq_.get(), filtered_clear_var_.get(), bank_size_);
	SolveForGainsGivenLambda(kLambdaBot, start_freq_, gains_eq_.get());
	const float power_bot =
	DotProduct(gains_eq_.get(), filtered_clear_var_.get(), bank_size_);
	if (power_target >= power_bot && power_target <= power_top) {
	SolveForLambda(power_target, power_bot, power_top);
	UpdateErbGains();
	} // Else experiencing variance underflow, so do nothing.
	}

	void IntelligibilityEnhancer::SolveForLambda(float power_target,
	float power_bot,
	float power_top) {
	const float kConvergeThresh = 0.001f; // TODO(ekmeyerson): Find best values
	const int kMaxIters = 100; // for these, based on experiments.

	const float reciprocal_power_target = 1.f / power_target;
	float lambda_bot = kLambdaBot;
	float lambda_top = kLambdaTop;
	float power_ratio = 2.0f; // Ratio of achieved power to target power.
	int iters = 0;
	while (std::fabs(power_ratio - 1.0f) > kConvergeThresh &&
	iters <= kMaxIters) {
	const float lambda = lambda_bot + (lambda_top - lambda_bot) / 2.0f;
	SolveForGainsGivenLambda(lambda, start_freq_, gains_eq_.get());
	const float power =
	DotProduct(gains_eq_.get(), filtered_clear_var_.get(), bank_size_);
	if (power < power_target) {
	lambda_bot = lambda;
	} else {
	lambda_top = lambda;
	}
	power_ratio = std::fabs(power * reciprocal_power_target);
	++iters;
	}
	}

	void IntelligibilityEnhancer::UpdateErbGains() {
	// (ERB gain) = filterbank' * (freq gain)
	float* gains = gain_applier_.target();
	for (int i = 0; i < freqs_; ++i) {
	gains[i] = 0.0f;
	for (int j = 0; j < bank_size_; ++j) {
	gains[i] = fmaf(filter_bank_[j][i], gains_eq_[j], gains[i]);
	}
	}
	}

	void IntelligibilityEnhancer::ProcessNoiseBlock(const complex<float>* in_block,
	complex<float>* /out_block/) {
	noise_variance_.Step(in_block);
	}

	int IntelligibilityEnhancer::GetBankSize(int sample_rate, int erb_resolution) {
	float freq_limit = sample_rate / 2000.0f;
	int erb_scale = ceilf(
	11.17f * logf((freq_limit + 0.312f) / (freq_limit + 14.6575f)) + 43.0f);
	return erb_scale * erb_resolution;
	}

	void IntelligibilityEnhancer::CreateErbBank() {
	int lf = 1, rf = 4;

	for (int i = 0; i < bank_size_; ++i) {
	float abs_temp = fabsf((i + 1.0f) / static_cast<float>(erb_resolution_));
	center_freqs_[i] = 676170.4f / (47.06538f - expf(0.08950404f * abs_temp));
	center_freqs_[i] -= 14678.49f;
	}
	float last_center_freq = center_freqs_[bank_size_ - 1];
	for (int i = 0; i < bank_size_; ++i) {
	center_freqs_[i] = 0.5f sample_rate_hz_ / last_center_freq;
	}

	for (int i = 0; i < bank_size_; ++i) {
	filter_bank_[i].resize(freqs_);
	}

	for (int i = 1; i <= bank_size_; ++i) {
	int lll, ll, rr, rrr;
	lll = round(center_freqs_[max(1, i - lf) - 1] * freqs_ /
	(0.5f * sample_rate_hz_));
	ll =
	round(center_freqs_[max(1, i) - 1] * freqs_ / (0.5f * sample_rate_hz_));
	lll = min(freqs_, max(lll, 1)) - 1;
	ll = min(freqs_, max(ll, 1)) - 1;

	rrr = round(center_freqs_[min(bank_size_, i + rf) - 1] * freqs_ /
	(0.5f * sample_rate_hz_));
	rr = round(center_freqs_[min(bank_size_, i + 1) - 1] * freqs_ /
	(0.5f * sample_rate_hz_));
	rrr = min(freqs_, max(rrr, 1)) - 1;
	rr = min(freqs_, max(rr, 1)) - 1;

	float step, element;

	step = 1.0f / (ll - lll);
	element = 0.0f;
	for (int j = lll; j <= ll; ++j) {
	filter_bank_[i - 1][j] = element;
	element += step;
	}
	step = 1.0f / (rrr - rr);
	element = 1.0f;
	for (int j = rr; j <= rrr; ++j) {
	filter_bank_[i - 1][j] = element;
	element -= step;
	}
	for (int j = ll; j <= rr; ++j) {
	filter_bank_[i - 1][j] = 1.0f;
	}
	}

	float sum;
	for (int i = 0; i < freqs_; ++i) {
	sum = 0.0f;
	for (int j = 0; j < bank_size_; ++j) {
	sum += filter_bank_[j][i];
	}
	for (int j = 0; j < bank_size_; ++j) {
	filter_bank_[j][i] /= sum;
	}
	}
	}

	void IntelligibilityEnhancer::SolveForGainsGivenLambda(float lambda,
	int start_freq,
	float* sols) {
	bool quadratic = (kConfigRho < 1.0f);
	const float* var_x0 = filtered_clear_var_.get();
	const float* var_n0 = filtered_noise_var_.get();

	for (int n = 0; n < start_freq; ++n) {
	sols[n] = 1.0f;
	}

	// Analytic solution for optimal gains. See paper for derivation.
	for (int n = start_freq - 1; n < bank_size_; ++n) {
	float alpha0, beta0, gamma0;
	gamma0 = 0.5f * rho_[n] * var_x0[n] * var_n0[n] +
	lambda * var_x0[n] * var_n0[n] * var_n0[n];
	beta0 = lambda * var_x0[n] * (2 - rho_[n]) * var_x0[n] * var_n0[n];
	if (quadratic) {
	alpha0 = lambda * var_x0[n] * (1 - rho_[n]) * var_x0[n] * var_x0[n];
	sols[n] =
	(-beta0 - sqrtf(beta0 * beta0 - 4 * alpha0 * gamma0)) / (2 * alpha0);
	} else {
	sols[n] = -gamma0 / beta0;
	}
	sols[n] = fmax(0, sols[n]);
	}
	}

	void IntelligibilityEnhancer::FilterVariance(const float* var, float* result) {
	DCHECK_GT(freqs_, 0);
	for (int i = 0; i < bank_size_; ++i) {
	result[i] = DotProduct(&filter_bank_[i][0], var, freqs_);
	}
	}

	float IntelligibilityEnhancer::DotProduct(const float* a,
	const float* b,
	int length) {
	float ret = 0.0f;

	for (int i = 0; i < length; ++i) {
	ret = fmaf(a[i], b[i], ret);
	}
	return ret;
	}

	} // namespace webrtc