Add new variance update option and unittests for intelligibility

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 145cc0872866db4effc4715000b979e54d5e723e..5426c444068f87965b05699c98de96dada565283 100644
--- a/webrtc/modules/audio_processing/intelligibility/intelligibility_utils.cc
+++ b/webrtc/modules/audio_processing/intelligibility/intelligibility_utils.cc
@@ -96,7 +96,8 @@ VarianceArray::VarianceArray(int freqs,
       decay_(decay),
       history_cursor_(0),
       count_(0),
-      array_mean_(0.0f) {
+      array_mean_(0.0f),
+      buffer_full_(false) {
   history_.reset(new rtc::scoped_ptr<complex<float>[]>[freqs_]());
   for (int i = 0; i < freqs_; ++i) {
     history_[i].reset(new complex<float>[window_size_]());
@@ -122,6 +123,9 @@ VarianceArray::VarianceArray(int freqs,
     case kStepBlocked:
       step_func_ = &VarianceArray::BlockedStep;
       break;
+    case kStepBlockBasedMovingAverage:
+      step_func_ = &VarianceArray::BlockBasedMovingAverage;
+      break;
   }
 }
 
@@ -223,7 +227,7 @@ void VarianceArray::WindowedStep(const complex<float>* data, bool /*dummy*/) {
 // 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*/) {
-  int blocks = min(window_size_, history_cursor_);
+  int blocks = min(window_size_, history_cursor_ + 1);
   for (int i = 0; i < freqs_; ++i) {
     AddToMean(data[i], count_ + 1, &sub_running_mean_[i]);
     AddToMean(data[i] * std::conj(data[i]), count_ + 1,
@@ -242,8 +246,8 @@ void VarianceArray::BlockedStep(const complex<float>* data, bool /*dummy*/) {
       running_mean_[i] = complex<float>(0.0f, 0.0f);
       running_mean_sq_[i] = complex<float>(0.0f, 0.0f);
       for (int j = 0; j < min(window_size_, history_cursor_); ++j) {
-        AddToMean(subhistory_[i][j], j, &running_mean_[i]);
-        AddToMean(subhistory_sq_[i][j], j, &running_mean_sq_[i]);
+        AddToMean(subhistory_[i][j], j + 1, &running_mean_[i]);
+        AddToMean(subhistory_sq_[i][j], j + 1, &running_mean_sq_[i]);
       }
       ++history_cursor_;
     }
@@ -254,6 +258,47 @@ void VarianceArray::BlockedStep(const complex<float>* data, bool /*dummy*/) {
   }
 }
 
+// Recomputes variances from scratch each window based on previous window.
+void VarianceArray::BlockBasedMovingAverage(

[turaj, 2015/06/26 00:32:58]

There is a concern, which is not proven, that keeping track of sum of the
elements in a circular buffer in the following fashion, i.e. subtract the oldest
and add the newest, might diverge from the true sum, over time. Although it
might not be the case for this application as a call typically is not so long. 

Could you please add a TODO that we add a counter so that after a while
|running_mean_| and |running_mean_sq_| should be replaces with their "true"
value by summing over all the element?

[ekm, 2015/06/26 19:07:09]

Interesting. This is just do to floating point errors? and we can't expect the
total error to converge to 0?

[turaj, 2015/06/29 17:33:35]

Something like that, but I'm not sure.

+    const std::complex<float>* data, bool /*dummy*/) {
+  for (int i = 0; i < 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 (int i = 0; i < 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()) * freqs_);
   memset(running_mean_sq_.get(), 0, sizeof(*running_mean_sq_.get()) * freqs_);