Fix and simplify the power estimation in the IntelligibilityEnhancer

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 7da9b957a422488957c00efd2f67b007dce617c0..6c44415130027aa736e2384cfd9557a10bc74347 100644
--- a/webrtc/modules/audio_processing/intelligibility/intelligibility_utils.cc
+++ b/webrtc/modules/audio_processing/intelligibility/intelligibility_utils.cc
@@ -8,10 +8,6 @@
  *  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>
@@ -19,271 +15,46 @@
 #include <string.h>
 #include <algorithm>
 
-using std::complex;
-using std::min;
-
 namespace webrtc {
 
 namespace intelligibility {
 
+namespace {
+
+// Return |current| changed towards |target|, with the change being at most
+// |limit|.
 float UpdateFactor(float target, float current, float limit) {
   float delta = fabsf(target - current);
-  float sign = copysign(1.0f, target - current);
+  float sign = copysign(1.f, 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);
-}
+}  // namespace
 
-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]()),
+PowerEstimator::PowerEstimator(size_t num_freqs,
+                               float decay)
+    : magnitude_(new float[num_freqs]()),
+      power_(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;
-  }
+      decay_(decay) {
+  memset(magnitude_.get(), 0, sizeof(*magnitude_.get()) * num_freqs_);
+  memset(power_.get(), 0, sizeof(*power_.get()) * num_freqs_);
 }
 
-// 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
+// Compute the magnitude from the beginning, with exponential decaying of the
 // series data.
-void VarianceArray::DecayStep(const complex<float>* data, bool /*dummy*/) {
-  array_mean_ = 0.0f;
-  ++count_;
+void PowerEstimator::Step(const std::complex<float>* data) {
   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);
+    magnitude_[i] = decay_ * magnitude_[i] +
+                (1.f - decay_) * std::abs(data[i]);
   }
 }
 
-// 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;
+const float* PowerEstimator::Power() {
   for (size_t i = 0; i < num_freqs_; ++i) {
-    variance_[i] *= scale * scale;
-    array_mean_ += (variance_[i] - array_mean_) / (i + 1);
+    power_[i] = magnitude_[i] * magnitude_[i];
   }
+  return &power_[0];
 }
 
 GainApplier::GainApplier(size_t freqs, float change_limit)
@@ -292,17 +63,17 @@ GainApplier::GainApplier(size_t freqs, float 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;
+    target_[i] = 1.f;
+    current_[i] = 1.f;
   }
 }
 
-void GainApplier::Apply(const complex<float>* in_block,
-                        complex<float>* out_block) {
+void GainApplier::Apply(const std::complex<float>* in_block,
+                        std::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;
+      factor = 1.f;
     }
     out_block[i] = factor * in_block[i];
     current_[i] = UpdateFactor(target_[i], current_[i], change_limit_);