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/*
* 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