Integrate Intelligibility with APM

webrtc/modules/audio_processing/intelligibility/intelligibility_utils.cc

Index: webrtc/modules/audio_processing/intelligibility/intelligibility_utils.cc
diff --git a/webrtc/modules/audio_processing/intelligibility/intelligibility_utils.cc b/webrtc/modules/audio_processing/intelligibility/intelligibility_utils.cc
index 00d9b536584360a9ad989c72751bdd6e64319356..2c2743f05323fef4597a3d2a20367105ae942e11 100644
--- a/webrtc/modules/audio_processing/intelligibility/intelligibility_utils.cc
+++ b/webrtc/modules/audio_processing/intelligibility/intelligibility_utils.cc
@@ -51,33 +51,33 @@ void AddToMean(complex<float> data, int count, complex<float>* mean) {
 
 static const int kWindowBlockSize = 10;
 
-VarianceArray::VarianceArray(int freqs,
+VarianceArray::VarianceArray(int num_freqs,
                              StepType type,
                              int window_size,
                              float decay)
-    : running_mean_(new complex<float>[freqs]()),
-      running_mean_sq_(new complex<float>[freqs]()),
-      sub_running_mean_(new complex<float>[freqs]()),
-      sub_running_mean_sq_(new complex<float>[freqs]()),
-      variance_(new float[freqs]()),
-      conj_sum_(new float[freqs]()),
-      freqs_(freqs),
+    : 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>[]>[freqs_]());
-  for (int i = 0; i < freqs_; ++i) {
+  history_.reset(new rtc::scoped_ptr<complex<float>[]>[num_freqs_]());
+  for (int i = 0; i < num_freqs_; ++i) {
     history_[i].reset(new complex<float>[window_size_]());
   }
-  subhistory_.reset(new rtc::scoped_ptr<complex<float>[]>[freqs_]());
-  for (int i = 0; i < freqs_; ++i) {
+  subhistory_.reset(new rtc::scoped_ptr<complex<float>[]>[num_freqs_]());
+  for (int i = 0; i < num_freqs_; ++i) {
     subhistory_[i].reset(new complex<float>[window_size_]());
   }
-  subhistory_sq_.reset(new rtc::scoped_ptr<complex<float>[]>[freqs_]());
-  for (int i = 0; i < freqs_; ++i) {
+  subhistory_sq_.reset(new rtc::scoped_ptr<complex<float>[]>[num_freqs_]());
+  for (int i = 0; i < num_freqs_; ++i) {
     subhistory_sq_[i].reset(new complex<float>[window_size_]());
   }
   switch (type) {
@@ -104,7 +104,7 @@ VarianceArray::VarianceArray(int freqs,
 void VarianceArray::InfiniteStep(const complex<float>* data, bool skip_fudge) {
   array_mean_ = 0.0f;
   ++count_;
-  for (int i = 0; i < freqs_; ++i) {
+  for (int i = 0; i < num_freqs_; ++i) {
     complex<float> sample = data[i];
     if (!skip_fudge) {
       sample = zerofudge(sample);
@@ -132,7 +132,7 @@ void VarianceArray::InfiniteStep(const complex<float>* data, bool skip_fudge) {
 void VarianceArray::DecayStep(const complex<float>* data, bool /*dummy*/) {
   array_mean_ = 0.0f;
   ++count_;
-  for (int i = 0; i < freqs_; ++i) {
+  for (int i = 0; i < num_freqs_; ++i) {
     complex<float> sample = data[i];
     sample = zerofudge(sample);
 
@@ -159,7 +159,7 @@ void VarianceArray::DecayStep(const complex<float>* data, bool /*dummy*/) {
 void VarianceArray::WindowedStep(const complex<float>* data, bool /*dummy*/) {
   int num = min(count_ + 1, window_size_);
   array_mean_ = 0.0f;
-  for (int i = 0; i < freqs_; ++i) {
+  for (int i = 0; i < num_freqs_; ++i) {
     complex<float> mean;
     float conj_sum = 0.0f;
 
@@ -192,7 +192,7 @@ void VarianceArray::WindowedStep(const complex<float>* data, bool /*dummy*/) {
 // are recomputed from scratch at each of these transitions.
 void VarianceArray::BlockedStep(const complex<float>* data, bool /*dummy*/) {
   int blocks = min(window_size_, history_cursor_ + 1);
-  for (int i = 0; i < freqs_; ++i) {
+  for (int 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]);
@@ -228,7 +228,7 @@ void VarianceArray::BlockBasedMovingAverage(const std::complex<float>* data,
   // 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 (int i = 0; i < freqs_; ++i) {
+  for (int i = 0; i < num_freqs_; ++i) {
     sub_running_mean_[i] += data[i];
     sub_running_mean_sq_[i] += data[i] * std::conj(data[i]);
   }
@@ -239,7 +239,7 @@ void VarianceArray::BlockBasedMovingAverage(const std::complex<float>* data,
   if (count_ >= kWindowBlockSize) {
     count_ = 0;
 
-    for (int i = 0; i < freqs_; ++i) {
+    for (int i = 0; i < num_freqs_; ++i) {
       running_mean_[i] -= subhistory_[i][history_cursor_];
       running_mean_sq_[i] -= subhistory_sq_[i][history_cursor_];
 
@@ -268,10 +268,11 @@ void VarianceArray::BlockBasedMovingAverage(const std::complex<float>* data,
 }
 
 void VarianceArray::Clear() {
-  memset(running_mean_.get(), 0, sizeof(*running_mean_.get()) * freqs_);
-  memset(running_mean_sq_.get(), 0, sizeof(*running_mean_sq_.get()) * freqs_);
-  memset(variance_.get(), 0, sizeof(*variance_.get()) * freqs_);
-  memset(conj_sum_.get(), 0, sizeof(*conj_sum_.get()) * freqs_);
+  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;
@@ -279,14 +280,14 @@ void VarianceArray::Clear() {
 
 void VarianceArray::ApplyScale(float scale) {
   array_mean_ = 0.0f;
-  for (int i = 0; i < freqs_; ++i) {
+  for (int i = 0; i < num_freqs_; ++i) {
     variance_[i] *= scale * scale;
     array_mean_ += (variance_[i] - array_mean_) / (i + 1);
   }
 }
 
 GainApplier::GainApplier(int freqs, float change_limit)
-    : freqs_(freqs),
+    : num_freqs_(freqs),
       change_limit_(change_limit),
       target_(new float[freqs]()),
       current_(new float[freqs]()) {
@@ -298,7 +299,7 @@ GainApplier::GainApplier(int freqs, float change_limit)
 
 void GainApplier::Apply(const complex<float>* in_block,
                         complex<float>* out_block) {
-  for (int i = 0; i < freqs_; ++i) {
+  for (int i = 0; i < num_freqs_; ++i) {
     float factor = sqrtf(fabsf(current_[i]));
     if (!std::isnormal(factor)) {
       factor = 1.0f;