webrtc/modules/audio_processing/intelligibility/intelligibility_utils.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 helper functions and classes for intelligibility enhancement.
 //

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

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

 using std::complex;
 using std::min;

 namespace webrtc {

 namespace intelligibility {

 float UpdateFactor(float target, float current, float limit) {
   float delta = fabsf(target - current);
   float sign = copysign(1.0f, target - current);
   return current + sign * fminf(delta, limit);
 }

 float AddDitherIfZero(float value) {
   return value == 0.f ? std::rand() * 0.01f / RAND_MAX : value;
 }

 complex<float> zerofudge(complex<float> c) {
   return complex<float>(AddDitherIfZero(c.real()), AddDitherIfZero(c.imag()));
 }

 complex<float> NewMean(complex<float> mean, complex<float> data, size_t count) {
   return mean + (data - mean) / static_cast<float>(count);
 }

 void AddToMean(complex<float> data, size_t count, complex<float>* mean) {
   (*mean) = NewMean(*mean, data, count);
 }


 static const size_t kWindowBlockSize = 10;

 VarianceArray::VarianceArray(size_t num_freqs,
                              StepType type,
                              size_t window_size,
                              float decay)
     : running_mean_(new complex<float>[num_freqs]()),
       running_mean_sq_(new complex<float>[num_freqs]()),
       sub_running_mean_(new complex<float>[num_freqs]()),
       sub_running_mean_sq_(new complex<float>[num_freqs]()),
       variance_(new float[num_freqs]()),
       conj_sum_(new float[num_freqs]()),
       num_freqs_(num_freqs),
       window_size_(window_size),
       decay_(decay),
       history_cursor_(0),
       count_(0),
       array_mean_(0.0f),
       buffer_full_(false) {
   history_.reset(new rtc::scoped_ptr<complex<float>[]>[num_freqs_]());
   for (size_t i = 0; i < num_freqs_; ++i) {
     history_[i].reset(new complex<float>[window_size_]());
   }
   subhistory_.reset(new rtc::scoped_ptr<complex<float>[]>[num_freqs_]());
   for (size_t i = 0; i < num_freqs_; ++i) {
     subhistory_[i].reset(new complex<float>[window_size_]());
   }
   subhistory_sq_.reset(new rtc::scoped_ptr<complex<float>[]>[num_freqs_]());
   for (size_t i = 0; i < num_freqs_; ++i) {
     subhistory_sq_[i].reset(new complex<float>[window_size_]());
   }
   switch (type) {
     case kStepInfinite:
       step_func_ = &VarianceArray::InfiniteStep;
       break;
     case kStepDecaying:
       step_func_ = &VarianceArray::DecayStep;
       break;
     case kStepWindowed:
       step_func_ = &VarianceArray::WindowedStep;
       break;
     case kStepBlocked:
       step_func_ = &VarianceArray::BlockedStep;
       break;
     case kStepBlockBasedMovingAverage:
       step_func_ = &VarianceArray::BlockBasedMovingAverage;
       break;
   }
 }

 // Compute the variance with Welford's algorithm, adding some fudge to
 // the input in case of all-zeroes.
 void VarianceArray::InfiniteStep(const complex<float>* data, bool skip_fudge) {
   array_mean_ = 0.0f;
   ++count_;
   for (size_t i = 0; i < num_freqs_; ++i) {
     complex<float> sample = data[i];
     if (!skip_fudge) {
       sample = zerofudge(sample);
     }
     if (count_ == 1) {
       running_mean_[i] = sample;
       variance_[i] = 0.0f;
     } else {
       float old_sum = conj_sum_[i];
       complex<float> old_mean = running_mean_[i];
       running_mean_[i] =
           old_mean + (sample - old_mean) / static_cast<float>(count_);
       conj_sum_[i] =
           (old_sum + std::conj(sample - old_mean) * (sample - running_mean_[i]))
               .real();
       variance_[i] =
           conj_sum_[i] / (count_ - 1);
     }
     array_mean_ += (variance_[i] - array_mean_) / (i + 1);
   }
 }

 // Compute the variance from the beginning, with exponential decaying of the
 // series data.
 void VarianceArray::DecayStep(const complex<float>* data, bool /*dummy*/) {
   array_mean_ = 0.0f;
   ++count_;
   for (size_t i = 0; i < num_freqs_; ++i) {
     complex<float> sample = data[i];
     sample = zerofudge(sample);

     if (count_ == 1) {
       running_mean_[i] = sample;
       running_mean_sq_[i] = sample * std::conj(sample);
       variance_[i] = 0.0f;
     } else {
       complex<float> prev = running_mean_[i];
       complex<float> prev2 = running_mean_sq_[i];
       running_mean_[i] = decay_ * prev + (1.0f - decay_) * sample;
       running_mean_sq_[i] =
           decay_ * prev2 + (1.0f - decay_) * sample * std::conj(sample);
       variance_[i] = (running_mean_sq_[i] -
                       running_mean_[i] * std::conj(running_mean_[i])).real();
     }

     array_mean_ += (variance_[i] - array_mean_) / (i + 1);
   }
 }

 // Windowed variance computation. On each step, the variances for the
 // window are recomputed from scratch, using Welford's algorithm.
 void VarianceArray::WindowedStep(const complex<float>* data, bool /*dummy*/) {
   size_t num = min(count_ + 1, window_size_);
   array_mean_ = 0.0f;
   for (size_t i = 0; i < num_freqs_; ++i) {
     complex<float> mean;
     float conj_sum = 0.0f;

     history_[i][history_cursor_] = data[i];

     mean = history_[i][history_cursor_];
     variance_[i] = 0.0f;
     for (size_t j = 1; j < num; ++j) {
       complex<float> sample =
           zerofudge(history_[i][(history_cursor_ + j) % window_size_]);
       sample = history_[i][(history_cursor_ + j) % window_size_];
       float old_sum = conj_sum;
       complex<float> old_mean = mean;

       mean = old_mean + (sample - old_mean) / static_cast<float>(j + 1);
       conj_sum =
           (old_sum + std::conj(sample - old_mean) * (sample - mean)).real();
       variance_[i] = conj_sum / (j);
     }
     array_mean_ += (variance_[i] - array_mean_) / (i + 1);
   }
   history_cursor_ = (history_cursor_ + 1) % window_size_;
   ++count_;
 }

 // Variance with a window of blocks. Within each block, the variances are
 // recomputed from scratch at every stp, using |Var(X) = E(X^2) - E^2(X)|.
 // Once a block is filled with kWindowBlockSize samples, it is added to the
 // history window and a new block is started. The variances for the window
 // are recomputed from scratch at each of these transitions.
 void VarianceArray::BlockedStep(const complex<float>* data, bool /*dummy*/) {
   size_t blocks = min(window_size_, history_cursor_ + 1);
   for (size_t i = 0; i < num_freqs_; ++i) {
     AddToMean(data[i], count_ + 1, &sub_running_mean_[i]);
     AddToMean(data[i] * std::conj(data[i]), count_ + 1,
               &sub_running_mean_sq_[i]);
     subhistory_[i][history_cursor_ % window_size_] = sub_running_mean_[i];
     subhistory_sq_[i][history_cursor_ % window_size_] = sub_running_mean_sq_[i];

     variance_[i] =
         (NewMean(running_mean_sq_[i], sub_running_mean_sq_[i], blocks) -
          NewMean(running_mean_[i], sub_running_mean_[i], blocks) *
              std::conj(NewMean(running_mean_[i], sub_running_mean_[i], blocks)))
             .real();
     if (count_ == kWindowBlockSize - 1) {
       sub_running_mean_[i] = complex<float>(0.0f, 0.0f);
       sub_running_mean_sq_[i] = complex<float>(0.0f, 0.0f);
       running_mean_[i] = complex<float>(0.0f, 0.0f);
       running_mean_sq_[i] = complex<float>(0.0f, 0.0f);
       for (size_t j = 0; j < min(window_size_, history_cursor_); ++j) {
         AddToMean(subhistory_[i][j], j + 1, &running_mean_[i]);
         AddToMean(subhistory_sq_[i][j], j + 1, &running_mean_sq_[i]);
       }
       ++history_cursor_;
     }
   }
   ++count_;
   if (count_ == kWindowBlockSize) {
     count_ = 0;
   }
 }

 // Recomputes variances for each window from scratch based on previous window.
 void VarianceArray::BlockBasedMovingAverage(const std::complex<float>* data,
                                             bool /*dummy*/) {
   // TODO(ekmeyerson) To mitigate potential divergence, add counter so that
   // after every so often sums are computed scratch by summing over all
   // elements instead of subtracting oldest and adding newest.
   for (size_t i = 0; i < num_freqs_; ++i) {
     sub_running_mean_[i] += data[i];
     sub_running_mean_sq_[i] += data[i] * std::conj(data[i]);
   }
   ++count_;

   // TODO(ekmeyerson) Make kWindowBlockSize nonconstant to allow
   // experimentation with different block size,window size pairs.
   if (count_ >= kWindowBlockSize) {
     count_ = 0;

     for (size_t i = 0; i < num_freqs_; ++i) {
       running_mean_[i] -= subhistory_[i][history_cursor_];
       running_mean_sq_[i] -= subhistory_sq_[i][history_cursor_];

       float scale = 1.f / kWindowBlockSize;
       subhistory_[i][history_cursor_] = sub_running_mean_[i] * scale;
       subhistory_sq_[i][history_cursor_] = sub_running_mean_sq_[i] * scale;

       sub_running_mean_[i] = std::complex<float>(0.0f, 0.0f);
       sub_running_mean_sq_[i] = std::complex<float>(0.0f, 0.0f);

       running_mean_[i] += subhistory_[i][history_cursor_];
       running_mean_sq_[i] += subhistory_sq_[i][history_cursor_];

       scale = 1.f / (buffer_full_ ? window_size_ : history_cursor_ + 1);
       variance_[i] = std::real(running_mean_sq_[i] * scale -
                                running_mean_[i] * scale *
                                    std::conj(running_mean_[i]) * scale);
     }

     ++history_cursor_;
     if (history_cursor_ >= window_size_) {
       buffer_full_ = true;
       history_cursor_ = 0;
     }
   }
 }

 void VarianceArray::Clear() {
   memset(running_mean_.get(), 0, sizeof(*running_mean_.get()) * num_freqs_);
   memset(running_mean_sq_.get(), 0,
          sizeof(*running_mean_sq_.get()) * num_freqs_);
   memset(variance_.get(), 0, sizeof(*variance_.get()) * num_freqs_);
   memset(conj_sum_.get(), 0, sizeof(*conj_sum_.get()) * num_freqs_);
   history_cursor_ = 0;
   count_ = 0;
   array_mean_ = 0.0f;
 }

 void VarianceArray::ApplyScale(float scale) {
   array_mean_ = 0.0f;
   for (size_t i = 0; i < num_freqs_; ++i) {
     variance_[i] *= scale * scale;
     array_mean_ += (variance_[i] - array_mean_) / (i + 1);
   }
 }

 GainApplier::GainApplier(size_t freqs, float change_limit)
     : num_freqs_(freqs),
       change_limit_(change_limit),
       target_(new float[freqs]()),
       current_(new float[freqs]()) {
   for (size_t i = 0; i < freqs; ++i) {
     target_[i] = 1.0f;
     current_[i] = 1.0f;
   }
 }

 void GainApplier::Apply(const complex<float>* in_block,
                         complex<float>* out_block) {
   for (size_t i = 0; i < num_freqs_; ++i) {
     float factor = sqrtf(fabsf(current_[i]));
     if (!std::isnormal(factor)) {
       factor = 1.0f;
     }
     out_block[i] = factor * in_block[i];
     current_[i] = UpdateFactor(target_[i], current_[i], change_limit_);
   }
 }

 }  // namespace intelligibility

 }  // 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 helper functions and classes for intelligibility enhancement.
	//

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

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

	using std::complex;
	using std::min;

	namespace webrtc {

	namespace intelligibility {

	float UpdateFactor(float target, float current, float limit) {
	float delta = fabsf(target - current);
	float sign = copysign(1.0f, target - current);
	return current + sign * fminf(delta, limit);
	}

	float AddDitherIfZero(float value) {
	return value == 0.f ? std::rand() * 0.01f / RAND_MAX : value;
	}

	complex<float> zerofudge(complex<float> c) {
	return complex<float>(AddDitherIfZero(c.real()), AddDitherIfZero(c.imag()));
	}

	complex<float> NewMean(complex<float> mean, complex<float> data, size_t count) {
	return mean + (data - mean) / static_cast<float>(count);
	}

	void AddToMean(complex<float> data, size_t count, complex<float>* mean) {
	(mean) = NewMean(mean, data, count);
	}


	static const size_t kWindowBlockSize = 10;

	VarianceArray::VarianceArray(size_t num_freqs,
	StepType type,
	size_t window_size,
	float decay)
	: running_mean_(new complex<float>[num_freqs]()),
	running_mean_sq_(new complex<float>[num_freqs]()),
	sub_running_mean_(new complex<float>[num_freqs]()),
	sub_running_mean_sq_(new complex<float>[num_freqs]()),
	variance_(new float[num_freqs]()),
	conj_sum_(new float[num_freqs]()),
	num_freqs_(num_freqs),
	window_size_(window_size),
	decay_(decay),
	history_cursor_(0),
	count_(0),
	array_mean_(0.0f),
	buffer_full_(false) {
	history_.reset(new rtc::scoped_ptr<complex<float>[]>[num_freqs_]());
	for (size_t i = 0; i < num_freqs_; ++i) {
	history_[i].reset(new complex<float>[window_size_]());
	}
	subhistory_.reset(new rtc::scoped_ptr<complex<float>[]>[num_freqs_]());
	for (size_t i = 0; i < num_freqs_; ++i) {
	subhistory_[i].reset(new complex<float>[window_size_]());
	}
	subhistory_sq_.reset(new rtc::scoped_ptr<complex<float>[]>[num_freqs_]());
	for (size_t i = 0; i < num_freqs_; ++i) {
	subhistory_sq_[i].reset(new complex<float>[window_size_]());
	}
	switch (type) {
	case kStepInfinite:
	step_func_ = &VarianceArray::InfiniteStep;
	break;
	case kStepDecaying:
	step_func_ = &VarianceArray::DecayStep;
	break;
	case kStepWindowed:
	step_func_ = &VarianceArray::WindowedStep;
	break;
	case kStepBlocked:
	step_func_ = &VarianceArray::BlockedStep;
	break;
	case kStepBlockBasedMovingAverage:
	step_func_ = &VarianceArray::BlockBasedMovingAverage;
	break;
	}
	}

	// Compute the variance with Welford's algorithm, adding some fudge to
	// the input in case of all-zeroes.
	void VarianceArray::InfiniteStep(const complex<float>* data, bool skip_fudge) {
	array_mean_ = 0.0f;
	++count_;
	for (size_t i = 0; i < num_freqs_; ++i) {
	complex<float> sample = data[i];
	if (!skip_fudge) {
	sample = zerofudge(sample);
	}
	if (count_ == 1) {
	running_mean_[i] = sample;
	variance_[i] = 0.0f;
	} else {
	float old_sum = conj_sum_[i];
	complex<float> old_mean = running_mean_[i];
	running_mean_[i] =
	old_mean + (sample - old_mean) / static_cast<float>(count_);
	conj_sum_[i] =
	(old_sum + std::conj(sample - old_mean) * (sample - running_mean_[i]))
	.real();
	variance_[i] =
	conj_sum_[i] / (count_ - 1);
	}
	array_mean_ += (variance_[i] - array_mean_) / (i + 1);
	}
	}

	// Compute the variance from the beginning, with exponential decaying of the
	// series data.
	void VarianceArray::DecayStep(const complex<float>* data, bool /dummy/) {
	array_mean_ = 0.0f;
	++count_;
	for (size_t i = 0; i < num_freqs_; ++i) {
	complex<float> sample = data[i];
	sample = zerofudge(sample);

	if (count_ == 1) {
	running_mean_[i] = sample;
	running_mean_sq_[i] = sample * std::conj(sample);
	variance_[i] = 0.0f;
	} else {
	complex<float> prev = running_mean_[i];
	complex<float> prev2 = running_mean_sq_[i];
	running_mean_[i] = decay_ * prev + (1.0f - decay_) * sample;
	running_mean_sq_[i] =
	decay_ * prev2 + (1.0f - decay_) * sample * std::conj(sample);
	variance_[i] = (running_mean_sq_[i] -
	running_mean_[i] * std::conj(running_mean_[i])).real();
	}

	array_mean_ += (variance_[i] - array_mean_) / (i + 1);
	}
	}

	// Windowed variance computation. On each step, the variances for the
	// window are recomputed from scratch, using Welford's algorithm.
	void VarianceArray::WindowedStep(const complex<float>* data, bool /dummy/) {
	size_t num = min(count_ + 1, window_size_);
	array_mean_ = 0.0f;
	for (size_t i = 0; i < num_freqs_; ++i) {
	complex<float> mean;
	float conj_sum = 0.0f;

	history_[i][history_cursor_] = data[i];

	mean = history_[i][history_cursor_];
	variance_[i] = 0.0f;
	for (size_t j = 1; j < num; ++j) {
	complex<float> sample =
	zerofudge(history_[i][(history_cursor_ + j) % window_size_]);
	sample = history_[i][(history_cursor_ + j) % window_size_];
	float old_sum = conj_sum;
	complex<float> old_mean = mean;

	mean = old_mean + (sample - old_mean) / static_cast<float>(j + 1);
	conj_sum =
	(old_sum + std::conj(sample - old_mean) * (sample - mean)).real();
	variance_[i] = conj_sum / (j);
	}
	array_mean_ += (variance_[i] - array_mean_) / (i + 1);
	}
	history_cursor_ = (history_cursor_ + 1) % window_size_;
	++count_;
	}

	// Variance with a window of blocks. Within each block, the variances are
	// recomputed from scratch at every stp, using \|Var(X) = E(X^2) - E^2(X)\|.
	// Once a block is filled with kWindowBlockSize samples, it is added to the
	// history window and a new block is started. The variances for the window
	// are recomputed from scratch at each of these transitions.
	void VarianceArray::BlockedStep(const complex<float>* data, bool /dummy/) {
	size_t blocks = min(window_size_, history_cursor_ + 1);
	for (size_t i = 0; i < num_freqs_; ++i) {
	AddToMean(data[i], count_ + 1, &sub_running_mean_[i]);
	AddToMean(data[i] * std::conj(data[i]), count_ + 1,
	&sub_running_mean_sq_[i]);
	subhistory_[i][history_cursor_ % window_size_] = sub_running_mean_[i];
	subhistory_sq_[i][history_cursor_ % window_size_] = sub_running_mean_sq_[i];

	variance_[i] =
	(NewMean(running_mean_sq_[i], sub_running_mean_sq_[i], blocks) -
	NewMean(running_mean_[i], sub_running_mean_[i], blocks) *
	std::conj(NewMean(running_mean_[i], sub_running_mean_[i], blocks)))
	.real();
	if (count_ == kWindowBlockSize - 1) {
	sub_running_mean_[i] = complex<float>(0.0f, 0.0f);
	sub_running_mean_sq_[i] = complex<float>(0.0f, 0.0f);
	running_mean_[i] = complex<float>(0.0f, 0.0f);
	running_mean_sq_[i] = complex<float>(0.0f, 0.0f);
	for (size_t j = 0; j < min(window_size_, history_cursor_); ++j) {
	AddToMean(subhistory_[i][j], j + 1, &running_mean_[i]);
	AddToMean(subhistory_sq_[i][j], j + 1, &running_mean_sq_[i]);
	}
	++history_cursor_;
	}
	}
	++count_;
	if (count_ == kWindowBlockSize) {
	count_ = 0;
	}
	}

	// Recomputes variances for each window from scratch based on previous window.
	void VarianceArray::BlockBasedMovingAverage(const std::complex<float>* data,
	bool /dummy/) {
	// TODO(ekmeyerson) To mitigate potential divergence, add counter so that
	// after every so often sums are computed scratch by summing over all
	// elements instead of subtracting oldest and adding newest.
	for (size_t i = 0; i < num_freqs_; ++i) {
	sub_running_mean_[i] += data[i];
	sub_running_mean_sq_[i] += data[i] * std::conj(data[i]);
	}
	++count_;

	// TODO(ekmeyerson) Make kWindowBlockSize nonconstant to allow
	// experimentation with different block size,window size pairs.
	if (count_ >= kWindowBlockSize) {
	count_ = 0;

	for (size_t i = 0; i < num_freqs_; ++i) {
	running_mean_[i] -= subhistory_[i][history_cursor_];
	running_mean_sq_[i] -= subhistory_sq_[i][history_cursor_];

	float scale = 1.f / kWindowBlockSize;
	subhistory_[i][history_cursor_] = sub_running_mean_[i] * scale;
	subhistory_sq_[i][history_cursor_] = sub_running_mean_sq_[i] * scale;

	sub_running_mean_[i] = std::complex<float>(0.0f, 0.0f);
	sub_running_mean_sq_[i] = std::complex<float>(0.0f, 0.0f);

	running_mean_[i] += subhistory_[i][history_cursor_];
	running_mean_sq_[i] += subhistory_sq_[i][history_cursor_];

	scale = 1.f / (buffer_full_ ? window_size_ : history_cursor_ + 1);
	variance_[i] = std::real(running_mean_sq_[i] * scale -
	running_mean_[i] * scale *
	std::conj(running_mean_[i]) * scale);
	}

	++history_cursor_;
	if (history_cursor_ >= window_size_) {
	buffer_full_ = true;
	history_cursor_ = 0;
	}
	}
	}

	void VarianceArray::Clear() {
	memset(running_mean_.get(), 0, sizeof(running_mean_.get()) num_freqs_);
	memset(running_mean_sq_.get(), 0,
	sizeof(running_mean_sq_.get()) num_freqs_);
	memset(variance_.get(), 0, sizeof(variance_.get()) num_freqs_);
	memset(conj_sum_.get(), 0, sizeof(conj_sum_.get()) num_freqs_);
	history_cursor_ = 0;
	count_ = 0;
	array_mean_ = 0.0f;
	}

	void VarianceArray::ApplyScale(float scale) {
	array_mean_ = 0.0f;
	for (size_t i = 0; i < num_freqs_; ++i) {
	variance_[i] = scale scale;
	array_mean_ += (variance_[i] - array_mean_) / (i + 1);
	}
	}

	GainApplier::GainApplier(size_t freqs, float change_limit)
	: num_freqs_(freqs),
	change_limit_(change_limit),
	target_(new float[freqs]()),
	current_(new float[freqs]()) {
	for (size_t i = 0; i < freqs; ++i) {
	target_[i] = 1.0f;
	current_[i] = 1.0f;
	}
	}

	void GainApplier::Apply(const complex<float>* in_block,
	complex<float>* out_block) {
	for (size_t i = 0; i < num_freqs_; ++i) {
	float factor = sqrtf(fabsf(current_[i]));
	if (!std::isnormal(factor)) {
	factor = 1.0f;
	}
	out_block[i] = factor * in_block[i];
	current_[i] = UpdateFactor(target_[i], current_[i], change_limit_);
	}
	}

	} // namespace intelligibility

	} // namespace webrtc