Initial SIE commit: migrating existing code

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
new file mode 100644
index 0000000000000000000000000000000000000000..e6fc3fa6a73b39aee82ea0e039e2ed6dc70a6f53
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+++ b/webrtc/modules/audio_processing/intelligibility/intelligibility_utils.cc
@@ -0,0 +1,287 @@
+/*
+ *  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.
+ */
+
+#include "webrtc/modules/audio_processing/intelligibility/intelligibility_utils.h"
+
+#include <algorithm>
+#include <cmath>
+#include <cstring>
+
+using std::complex;
+
+namespace {
+
+// Return |current| changed towards |target|, with the change being at most
+// |limit|.
+inline 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);
+}
+
+// std::isfinite for complex numbers.
+inline bool cplxfinite(complex<float> c) {
+  return std::isfinite(c.real()) && std::isfinite(c.imag());
+}
+
+// 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}
+  };
+  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) {
+  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,
+                             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 scoped_ptr<complex<float>[]>[freqs_]());
+  for (int i = 0; i < freqs_; ++i) {
+    history_[i].reset(new complex<float>[window_size_]());
+  }
+  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 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;
+    case kStepWindowed:
+      step_func_ = &VarianceArray::WindowedStep;
+      break;
+    case kStepBlocked:
+      step_func_ = &VarianceArray::BlockedStep;
+      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 (int i = 0; i < 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); // + fudge[fudge_index].real();
+      if (skip_fudge && false) {
+        //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();
+    }
+
+    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_]);
+      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*/) {
+  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();
+    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_;
+    }
+  }
+  ++count_;
+  if (count_ == kWindowBlockSize) {
+    count_ = 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_);
+  memset(variance_.get(), 0, sizeof(*variance_.get()) * freqs_);
+  memset(conj_sum_.get(), 0, sizeof(*conj_sum_.get()) * freqs_);
+  history_cursor_ = 0;
+  count_ = 0;
+  array_mean_ = 0.0f;
+}
+
+void VarianceArray::ApplyScale(float scale) {
+  array_mean_ = 0.0f;
+  for (int i = 0; i < freqs_; ++i) {
+    variance_[i] *= scale * scale;
+    array_mean_ += (variance_[i] - array_mean_) / (i + 1);
+  }
+}
+
+GainApplier::GainApplier(int freqs, float change_limit)
+    : freqs_(freqs),
+      change_limit_(change_limit),
+      target_(new float[freqs]()),
+      current_(new float[freqs]()) {
+  for (int 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 (int i = 0; i < 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
+