Revert of Allow intelligibility to compile in apm

webrtc/modules/audio_processing/intelligibility/intelligibility_utils.cc

 /*
  *  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 <algorithm>
 #include <cmath>
 #include <cstring>
 
 using std::complex;
 
 namespace {
 
@@ skipping 12 matching lines @@
 
 // std::isnormal for complex numbers.
 inline bool cplxnormal(complex<float> c) {
   return std::isnormal(c.real()) && std::isnormal(c.imag());
 }
 
 // Apply a small fudge to degenerate complex values. The numbers in the array
 // were chosen randomly, so that even a series of all zeroes has some small
 // variability.
 inline complex<float> zerofudge(complex<float> c) {
-  const static complex<float> fudge[7] = {{0.001f, 0.002f},
-                                          {0.008f, 0.001f},
-                                          {0.003f, 0.008f},
-                                          {0.0006f, 0.0009f},
-                                          {0.001f, 0.004f},
-                                          {0.003f, 0.004f},
-                                          {0.002f, 0.009f}};
+  const static complex<float> fudge[7] = {
+    {0.001f, 0.002f}, {0.008f, 0.001f}, {0.003f, 0.008f}, {0.0006f, 0.0009f},
+    {0.001f, 0.004f}, {0.003f, 0.004f}, {0.002f, 0.009f}
+  };
   static int fudge_index = 0;
   if (cplxfinite(c) && !cplxnormal(c)) {
     fudge_index = (fudge_index + 1) % 7;
     return c + fudge[fudge_index];
   }
   return c;
 }
 
 // Incremental mean computation. Return the mean of the series with the
 // mean |mean| with added |data|.
-inline complex<float> NewMean(complex<float> mean,
-                              complex<float> data,
-                              int count) {
+inline complex<float> NewMean(complex<float> mean, complex<float> data,
+                                 int count) {
   return mean + (data - mean) / static_cast<float>(count);
 }
 
 inline void AddToMean(complex<float> data, int count, complex<float>* mean) {
   (*mean) = NewMean(*mean, data, count);
 }
 
 }  // namespace
 
 using std::min;
 
 namespace webrtc {
 
 namespace intelligibility {
 
 static const int kWindowBlockSize = 10;
 
-VarianceArray::VarianceArray(int freqs,
-                             StepType type,
-                             int window_size,
+VarianceArray::VarianceArray(int 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),
       window_size_(window_size),
       decay_(decay),
       history_cursor_(0),
       count_(0),
       array_mean_(0.0f) {
-  history_.reset(new rtc::scoped_ptr<complex<float>[]>[freqs_]());
+  history_.reset(new scoped_ptr<complex<float>[]>[freqs_]());
   for (int i = 0; i < freqs_; ++i) {
     history_[i].reset(new complex<float>[window_size_]());
   }
-  subhistory_.reset(new rtc::scoped_ptr<complex<float>[]>[freqs_]());
+  subhistory_.reset(new scoped_ptr<complex<float>[]>[freqs_]());
   for (int i = 0; i < freqs_; ++i) {
     subhistory_[i].reset(new complex<float>[window_size_]());
   }
-  subhistory_sq_.reset(new rtc::scoped_ptr<complex<float>[]>[freqs_]());
+  subhistory_sq_.reset(new scoped_ptr<complex<float>[]>[freqs_]());
   for (int i = 0; i < 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;
@@ skipping 15 matching lines @@
     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);  // + fudge[fudge_index].real();
+      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); // + fudge[fudge_index].real();
       if (skip_fudge && false) {
-        // variance_[i] -= fudge[fudge_index].real();
+        //variance_[i] -= fudge[fudge_index].real();
       }
     }
     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 (int i = 0; i < 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] = decay_ * variance_[i] + (1.0f - decay_) * (
-      //  (sample - running_mean_[i]) * std::conj(sample -
-      //  running_mean_[i])).real();
-      variance_[i] = (running_mean_sq_[i] -
-                      running_mean_[i] * std::conj(running_mean_[i])).real();
+      running_mean_sq_[i] = decay_ * prev2 +
+        (1.0f - decay_) * sample * std::conj(sample);
+      //variance_[i] = decay_ * variance_[i] + (1.0f - decay_) * (
+      //  (sample - running_mean_[i]) * std::conj(sample - running_mean_[i])).real();
+      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*/) {
   int num = min(count_ + 1, window_size_);
   array_mean_ = 0.0f;
   for (int i = 0; i < 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 (int j = 1; j < num; ++j) {
-      complex<float> sample =
-          zerofudge(history_[i][(history_cursor_ + j) % window_size_]);
+      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();
+      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*/) {
   int blocks = min(window_size_, history_cursor_);
   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,
               &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();
+    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 (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]);
       }
       ++history_cursor_;
@@ skipping 42 matching lines @@
       factor = 1.0f;
     }
     out_block[i] = factor * in_block[i];
     current_[i] = UpdateFactor(target_[i], current_[i], change_limit_);
   }
 }
 
 }  // namespace intelligibility
 
 }  // namespace webrtc
+