diff --git a/webrtc/modules/audio_processing/intelligibility/intelligibility_enhancer.cc b/webrtc/modules/audio_processing/intelligibility/intelligibility_enhancer.cc
new file mode 100644
index 0000000..932eff1
--- /dev/null
+++ b/webrtc/modules/audio_processing/intelligibility/intelligibility_enhancer.cc
@@ -0,0 +1,383 @@
+/*
+ *  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_enhancer.h"
+
+#include <cmath>
+#include <cstdlib>
+
+#include <algorithm>
+
+#include "webrtc/base/checks.h"
+#include "webrtc/common_audio/vad/include/webrtc_vad.h"
+#include "webrtc/common_audio/window_generator.h"
+
+using std::complex;
+using std::max;
+using std::min;
+
+namespace webrtc {
+
+const int IntelligibilityEnhancer::kErbResolution = 2;
+const int IntelligibilityEnhancer::kWindowSizeMs = 2;
+// The size of the chunk provided by APM, in milliseconds.
+const int IntelligibilityEnhancer::kChunkSizeMs = 10;
+const int IntelligibilityEnhancer::kAnalyzeRate = 800;
+const int IntelligibilityEnhancer::kVarianceRate = 2;
+const float IntelligibilityEnhancer::kClipFreq = 200.0f;
+const float IntelligibilityEnhancer::kConfigRho = 0.02f;
+const float IntelligibilityEnhancer::kKbdAlpha = 1.5f;
+const float IntelligibilityEnhancer::kGainChangeLimit = 0.0125f;
+
+using VarianceType = intelligibility::VarianceArray::StepType;
+
+IntelligibilityEnhancer::TransformCallback::TransformCallback(
+    IntelligibilityEnhancer* parent,
+    IntelligibilityEnhancer::AudioSource source)
+    : parent_(parent),
+      source_(source) {}
+
+void IntelligibilityEnhancer::TransformCallback::ProcessAudioBlock(
+    const complex<float>* const* in_block,
+    int in_channels, int frames, int /* out_channels */,
+    complex<float>* const* out_block) {
+  DCHECK_EQ(parent_->freqs_, frames);
+  for (int i = 0; i < in_channels; ++i) {
+    parent_->DispatchAudio(source_, in_block[i], out_block[i]);
+  }
+}
+
+IntelligibilityEnhancer::IntelligibilityEnhancer(int erb_resolution,
+                                                 int sample_rate_hz,
+                                                 int channels,
+                                                 int cv_type, float cv_alpha,
+                                                 int cv_win,
+                                                 int analysis_rate,
+                                                 int variance_rate,
+                                                 float gain_limit)
+    : freqs_(RealFourier::ComplexLength(RealFourier::FftOrder(
+          sample_rate_hz * kWindowSizeMs / 1000))),
+      window_size_(1 << RealFourier::FftOrder(freqs_)),
+      chunk_length_(sample_rate_hz * kChunkSizeMs / 1000),
+      bank_size_(GetBankSize(sample_rate_hz, erb_resolution)),
+      sample_rate_hz_(sample_rate_hz),
+      erb_resolution_(erb_resolution),
+      channels_(channels),
+      analysis_rate_(analysis_rate),
+      variance_rate_(variance_rate),
+      clear_variance_(freqs_, static_cast<VarianceType>(cv_type), cv_win,
+                      cv_alpha),
+      noise_variance_(freqs_, VarianceType::kStepInfinite, 475, 0.01f),
+      filtered_clear_var_(new float[bank_size_]),
+      filtered_noise_var_(new float[bank_size_]),
+      filter_bank_(nullptr),
+      center_freqs_(new float[bank_size_]),
+      rho_(new float[bank_size_]),
+      gains_eq_(new float[bank_size_]),
+      gain_applier_(freqs_, gain_limit),
+      temp_out_buffer_(nullptr),
+      input_audio_(new float*[channels]),
+      kbd_window_(new float[window_size_]),
+      render_callback_(this, AudioSource::kRenderStream),
+      capture_callback_(this, AudioSource::kCaptureStream),
+      block_count_(0),
+      analysis_step_(0),
+      vad_high_(nullptr),
+      vad_low_(nullptr),
+      vad_tmp_buffer_(new int16_t[chunk_length_]) {
+  DCHECK_LE(kConfigRho, 1.0f);
+
+  CreateErbBank();
+
+  WebRtcVad_Create(&vad_high_);
+  WebRtcVad_Init(vad_high_);
+  WebRtcVad_set_mode(vad_high_, 0);  // high likelihood of speech
+  WebRtcVad_Create(&vad_low_);
+  WebRtcVad_Init(vad_low_);
+  WebRtcVad_set_mode(vad_low_, 3);  // low likelihood of speech
+
+  temp_out_buffer_ = static_cast<float**>(malloc(
+      sizeof(*temp_out_buffer_) * channels_ +
+      sizeof(**temp_out_buffer_) * chunk_length_ * channels_));
+  for (int i = 0; i < channels_; ++i) {
+    temp_out_buffer_[i] = reinterpret_cast<float*>(temp_out_buffer_ + channels_)
+        + chunk_length_ * i;
+  }
+
+  for (int i = 0; i < bank_size_; ++i) {
+    rho_[i] = kConfigRho * kConfigRho;
+  }
+
+  float freqs_khz = kClipFreq / 1000.0f;
+  int erb_index = static_cast<int>(ceilf(11.17f * logf((freqs_khz + 0.312f) /
+                                                       (freqs_khz + 14.6575f))
+                                         + 43.0f));
+  start_freq_ = max(1, erb_index * kErbResolution);
+
+  WindowGenerator::KaiserBesselDerived(kKbdAlpha, window_size_,
+                                       kbd_window_.get());
+  render_mangler_.reset(new LappedTransform(channels_, channels_,
+                                            chunk_length_,
+                                            kbd_window_.get(),
+                                            window_size_,
+                                            window_size_ / 2,
+                                            &render_callback_));
+  capture_mangler_.reset(new LappedTransform(channels_, channels_,
+                                             chunk_length_,
+                                             kbd_window_.get(),
+                                             window_size_,
+                                             window_size_ / 2,
+                                             &capture_callback_));
+}
+
+IntelligibilityEnhancer::~IntelligibilityEnhancer() {
+  WebRtcVad_Free(vad_low_);
+  WebRtcVad_Free(vad_high_);
+  free(filter_bank_);
+}
+
+void IntelligibilityEnhancer::ProcessRenderAudio(float* const* audio) {
+  for (int i = 0; i < chunk_length_; ++i) {
+    vad_tmp_buffer_[i] = (int16_t)audio[0][i];
+  }
+  has_voice_low_ = WebRtcVad_Process(vad_low_, sample_rate_hz_,
+                                     vad_tmp_buffer_.get(), chunk_length_) == 1;
+
+  render_mangler_->ProcessChunk(audio, temp_out_buffer_);
+  for (int i = 0; i < channels_; ++i) {
+    memcpy(audio[i], temp_out_buffer_[i],
+           chunk_length_ * sizeof(**temp_out_buffer_));
+  }
+}
+
+void IntelligibilityEnhancer::ProcessCaptureAudio(float* const* audio) {
+  for (int i = 0; i < chunk_length_; ++i) {
+    vad_tmp_buffer_[i] = (int16_t)audio[0][i];
+  }
+  // TODO(bercic): the VAD was always detecting voice in the noise stream,
+  // no matter what the aggressiveness, so it was temporarily disabled here
+
+  //if (WebRtcVad_Process(vad_high_, sample_rate_hz_, vad_tmp_buffer_.get(),
+  //    chunk_length_) == 1) {
+  //  printf("capture HAS speech\n");
+  //  return;
+  //}
+  //printf("capture NO speech\n");
+  capture_mangler_->ProcessChunk(audio, temp_out_buffer_);
+}
+
+void IntelligibilityEnhancer::DispatchAudio(
+    IntelligibilityEnhancer::AudioSource source,
+    const complex<float>* in_block, complex<float>* out_block) {
+  switch (source) {
+    case kRenderStream:
+      ProcessClearBlock(in_block, out_block);
+      break;
+    case kCaptureStream:
+      ProcessNoiseBlock(in_block, out_block);
+      break;
+  }
+}
+
+void IntelligibilityEnhancer::ProcessClearBlock(const complex<float>* in_block,
+                                                complex<float>* out_block) {
+  float power_target;
+
+  if (block_count_ < 2) {
+    memset(out_block, 0, freqs_ * sizeof(*out_block));
+    ++block_count_;
+    return;
+  }
+
+  if (has_voice_low_ || true) {
+    clear_variance_.Step(in_block, false);
+    power_target = std::accumulate(clear_variance_.variance(),
+                                   clear_variance_.variance() + freqs_, 0.0f);
+
+    if (block_count_ % analysis_rate_ == analysis_rate_ - 1) {
+      AnalyzeClearBlock(power_target);
+      ++analysis_step_;
+      if (analysis_step_ == variance_rate_) {
+        analysis_step_ = 0;
+        clear_variance_.Clear();
+        noise_variance_.Clear();
+      }
+    }
+    ++block_count_;
+  }
+
+  /* efidata(n,:) = sqrt(b(n)) * fidata(n,:) */
+  gain_applier_.Apply(in_block, out_block);
+}
+
+void IntelligibilityEnhancer::AnalyzeClearBlock(float power_target) {
+  FilterVariance(clear_variance_.variance(), filtered_clear_var_.get());
+  FilterVariance(noise_variance_.variance(), filtered_noise_var_.get());
+
+  /* lambda binary search */
+
+  float lambda_bot = -1.0f, lambda_top = -10e-18f, lambda;
+  float power_bot, power_top, power;
+  SolveEquation14(lambda_top, start_freq_, gains_eq_.get());
+  power_top = DotProduct(gains_eq_.get(), filtered_clear_var_.get(),
+                         bank_size_);
+  SolveEquation14(lambda_bot, start_freq_, gains_eq_.get());
+  power_bot = DotProduct(gains_eq_.get(), filtered_clear_var_.get(),
+                         bank_size_);
+  DCHECK(power_target >= power_bot && power_target <= power_top);
+
+  float power_ratio = 2.0f;
+  int iters = 0;
+  while (fabs(power_ratio - 1.0f) > 0.001f && iters <= 100) {
+    lambda = lambda_bot + (lambda_top - lambda_bot) / 2.0f;
+    SolveEquation14(lambda, start_freq_, gains_eq_.get());
+    power = DotProduct(gains_eq_.get(), filtered_clear_var_.get(), bank_size_);
+    if (power < power_target) {
+      lambda_bot = lambda;
+    } else {
+      lambda_top = lambda;
+    }
+    power_ratio = fabs(power / power_target);
+    ++iters;
+  }
+
+  /* b = filterbank' * b */
+  float* gains = gain_applier_.target();
+  for (int i = 0; i < freqs_; ++i) {
+    gains[i] = 0.0f;
+    for (int j = 0; j < bank_size_; ++j) {
+      gains[i] = fmaf(filter_bank_[j][i], gains_eq_[j], gains[i]);
+    }
+  }
+}
+
+void IntelligibilityEnhancer::ProcessNoiseBlock(const complex<float>* in_block,
+                                                complex<float>* /*out_block*/) {
+  noise_variance_.Step(in_block);
+}
+
+int IntelligibilityEnhancer::GetBankSize(int sample_rate, int erb_resolution) {
+  float freq_limit = sample_rate / 2000.0f;
+  int erb_scale = ceilf(11.17f * logf((freq_limit + 0.312f) /
+                                      (freq_limit + 14.6575f)) + 43.0f);
+  return erb_scale * erb_resolution;
+}
+
+void IntelligibilityEnhancer::CreateErbBank() {
+  int lf = 1, rf = 4;
+
+  for (int i = 0; i < bank_size_; ++i) {
+    float abs_temp = fabsf((i + 1.0f) / static_cast<float>(erb_resolution_));
+    center_freqs_[i] = 676170.4f / (47.06538f - expf(0.08950404f * abs_temp));
+    center_freqs_[i] -= 14678.49f;
+  }
+  float last_center_freq = center_freqs_[bank_size_ - 1];
+  for (int i = 0; i < bank_size_; ++i) {
+    center_freqs_[i] *= 0.5f * sample_rate_hz_ / last_center_freq;
+  }
+
+  filter_bank_ = static_cast<float**>(malloc(
+      sizeof(*filter_bank_) * bank_size_ +
+      sizeof(**filter_bank_) * freqs_ * bank_size_));
+  for (int i = 0; i < bank_size_; ++i) {
+    filter_bank_[i] = reinterpret_cast<float*>(filter_bank_ + bank_size_) +
+        freqs_ * i;
+  }
+
+  for (int i = 1; i <= bank_size_; ++i) {
+    int lll, ll, rr, rrr;
+    lll = round(center_freqs_[max(1, i - lf) - 1] * freqs_ /
+        (0.5f * sample_rate_hz_));
+    ll  = round(center_freqs_[max(1, i     ) - 1] * freqs_ /
+        (0.5f * sample_rate_hz_));
+    lll = min(freqs_, max(lll, 1)) - 1;
+    ll  = min(freqs_, max(ll, 1)) - 1;
+
+    rrr = round(center_freqs_[min(bank_size_, i + rf) - 1] * freqs_ /
+        (0.5f * sample_rate_hz_));
+    rr  = round(center_freqs_[min(bank_size_, i + 1)  - 1] * freqs_ /
+        (0.5f * sample_rate_hz_));
+    rrr = min(freqs_, max(rrr, 1)) - 1;
+    rr  = min(freqs_, max(rr, 1)) - 1;
+
+    float step, element;
+
+    step = 1.0f / (ll - lll);
+    element = 0.0f;
+    for (int j = lll; j <= ll; ++j) {
+      filter_bank_[i - 1][j] = element;
+      element += step;
+    }
+    step = 1.0f / (rrr - rr);
+    element = 1.0f;
+    for (int j = rr; j <= rrr; ++j) {
+      filter_bank_[i - 1][j] = element;
+      element -= step;
+    }
+    for (int j = ll; j <= rr; ++j) {
+      filter_bank_[i - 1][j] = 1.0f;
+    }
+  }
+
+  float sum;
+  for (int i = 0; i < freqs_; ++i) {
+    sum = 0.0f;
+    for (int j = 0; j < bank_size_; ++j) {
+      sum += filter_bank_[j][i];
+    }
+    for (int j = 0; j < bank_size_; ++j) {
+      filter_bank_[j][i] /= sum;
+    }
+  }
+}
+
+void IntelligibilityEnhancer::SolveEquation14(float lambda, int start_freq,
+                                              float* sols) {
+  bool quadratic = (kConfigRho < 1.0f);
+  const float* var_x0 = filtered_clear_var_.get();
+  const float* var_n0 = filtered_noise_var_.get();
+
+  for (int n = 0; n < start_freq; ++n) {
+    sols[n] = 1.0f;
+  }
+  for (int n = start_freq - 1; n < bank_size_; ++n) {
+    float alpha0, beta0, gamma0;
+    gamma0 = 0.5f * rho_[n] * var_x0[n] * var_n0[n] +
+        lambda * var_x0[n] * var_n0[n] * var_n0[n];
+    beta0 = lambda * var_x0[n] * (2 - rho_[n]) * var_x0[n] * var_n0[n];
+    if (quadratic) {
+      alpha0 = lambda * var_x0[n] * (1 - rho_[n]) * var_x0[n] * var_x0[n];
+      sols[n] = (-beta0 - sqrtf(beta0 * beta0 - 4 * alpha0 * gamma0))
+          / (2 * alpha0);
+    } else {
+      sols[n] = -gamma0 / beta0;
+    }
+    sols[n] = fmax(0, sols[n]);
+  }
+}
+
+void IntelligibilityEnhancer::FilterVariance(const float* var, float* result) {
+  for (int i = 0; i < bank_size_; ++i) {
+    result[i] = DotProduct(filter_bank_[i], var, freqs_);
+  }
+}
+
+float IntelligibilityEnhancer::DotProduct(const float* a, const float* b,
+                                               int length) {
+  float ret = 0.0f;
+
+  for (int i = 0; i < length; ++i) {
+    ret = fmaf(a[i], b[i], ret);
+  }
+  return ret;
+}
+
+}  // namespace webrtc
+
diff --git a/webrtc/modules/audio_processing/intelligibility/intelligibility_enhancer.h b/webrtc/modules/audio_processing/intelligibility/intelligibility_enhancer.h
new file mode 100644
index 0000000..d0818f6
--- /dev/null
+++ b/webrtc/modules/audio_processing/intelligibility/intelligibility_enhancer.h
@@ -0,0 +1,137 @@
+/*
+ *  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.
+ */
+
+#ifndef WEBRTC_MODULES_AUDIO_PROCESSING_INTELLIGIBILITY_INTELLIGIBILITY_ENHANCER_H_
+#define WEBRTC_MODULES_AUDIO_PROCESSING_INTELLIGIBILITY_INTELLIGIBILITY_ENHANCER_H_
+
+#include <complex>
+
+#include "webrtc/common_audio/lapped_transform.h"
+#include "webrtc/modules/audio_processing/intelligibility/intelligibility_utils.h"
+#include "webrtc/system_wrappers/interface/scoped_ptr.h"
+
+struct WebRtcVadInst;
+typedef struct WebRtcVadInst VadInst;
+
+namespace webrtc {
+
+// Speech intelligibility enhancement module. Reads render and capture
+// audio streams and modifies the render stream with a set of gains per
+// frequency bin to enhance speech against the noise background.
+class IntelligibilityEnhancer {
+ public:
+  // Construct a new instance with the given filter bank resolution,
+  // sampling rate, number of channels and analysis rates.
+  // |analysis_rate| sets the number of input blocks (containing speech!)
+  // to elapse before a new gain computation is made. |variance_rate| specifies
+  // the number of gain recomputations after which the variances are reset.
+  // |cv_*| are parameters for the VarianceArray constructor for the
+  // lear speech stream.
+  // TODO(bercic): the |cv_*|, |*_rate| and |gain_limit| parameters should
+  // probably go away once fine tuning is done. They override the internal
+  // constants in the class (kGainChangeLimit, kAnalyzeRate, kVarianceRate).
+  IntelligibilityEnhancer(int erb_resolution, int sample_rate_hz, int channels,
+                          int cv_type, float cv_alpha, int cv_win,
+                          int analysis_rate, int variance_rate,
+                          float gain_limit);
+  ~IntelligibilityEnhancer();
+
+  void ProcessRenderAudio(float* const* audio);
+  void ProcessCaptureAudio(float* const* audio);
+
+ private:
+  enum AudioSource {
+    kRenderStream = 0,
+    kCaptureStream,
+  };
+
+  class TransformCallback : public LappedTransform::Callback {
+   public:
+    TransformCallback(IntelligibilityEnhancer* parent, AudioSource source);
+    virtual void ProcessAudioBlock(const std::complex<float>* const* in_block,
+                                   int in_channels, int frames,
+                                   int out_channels,
+                                   std::complex<float>* const* out_block);
+
+   private:
+    IntelligibilityEnhancer* parent_;
+    AudioSource source_;
+  };
+  friend class TransformCallback;
+
+  void DispatchAudio(AudioSource source, const std::complex<float>* in_block,
+                     std::complex<float>* out_block);
+  void ProcessClearBlock(const std::complex<float>* in_block,
+                         std::complex<float>* out_block);
+  void AnalyzeClearBlock(float power_target);
+  void ProcessNoiseBlock(const std::complex<float>* in_block,
+                         std::complex<float>* out_block);
+
+  static int GetBankSize(int sample_rate, int erb_resolution);
+  void CreateErbBank();
+  void SolveEquation14(float lambda, int start_freq, float* sols);
+  void FilterVariance(const float* var, float* result);
+  static float DotProduct(const float* a, const float* b, int length);
+
+  static const int kErbResolution;
+  static const int kWindowSizeMs;
+  static const int kChunkSizeMs;
+  static const int kAnalyzeRate;
+  static const int kVarianceRate;
+  static const float kClipFreq;
+  static const float kConfigRho;
+  static const float kKbdAlpha;
+  static const float kGainChangeLimit;
+
+  const int freqs_;
+  const int window_size_;  // window size in samples; also the block size
+  const int chunk_length_;  // chunk size in samples
+  const int bank_size_;
+  const int sample_rate_hz_;
+  const int erb_resolution_;
+  const int channels_;
+  const int analysis_rate_;
+  const int variance_rate_;
+
+  intelligibility::VarianceArray clear_variance_;
+  intelligibility::VarianceArray noise_variance_;
+  scoped_ptr<float[]> filtered_clear_var_;
+  scoped_ptr<float[]> filtered_noise_var_;
+  float** filter_bank_;
+  scoped_ptr<float[]> center_freqs_;
+  int start_freq_;
+  scoped_ptr<float[]> rho_;
+  scoped_ptr<float[]> gains_eq_;
+  intelligibility::GainApplier gain_applier_;
+
+  // Destination buffer used to reassemble blocked chunks before overwriting
+  // the original input array with modifications.
+  float** temp_out_buffer_;
+  scoped_ptr<float*[]> input_audio_;
+  scoped_ptr<float[]> kbd_window_;
+  TransformCallback render_callback_;
+  TransformCallback capture_callback_;
+  scoped_ptr<LappedTransform> render_mangler_;
+  scoped_ptr<LappedTransform> capture_mangler_;
+  int block_count_;
+  int analysis_step_;
+
+  // TODO(bercic): Quick stopgap measure for voice detection in the clear
+  // and noise streams.
+  VadInst* vad_high_;
+  VadInst* vad_low_;
+  scoped_ptr<int16_t[]> vad_tmp_buffer_;
+  bool has_voice_low_;
+};
+
+}  // namespace webrtc
+
+#endif  // WEBRTC_MODULES_AUDIO_PROCESSING_INTELLIGIBILITY_INTELLIGIBILITY_ENHANCER_H_
+
diff --git a/webrtc/modules/audio_processing/intelligibility/intelligibility_proc.cc b/webrtc/modules/audio_processing/intelligibility/intelligibility_proc.cc
new file mode 100644
index 0000000..b0ea2df
--- /dev/null
+++ b/webrtc/modules/audio_processing/intelligibility/intelligibility_proc.cc
@@ -0,0 +1,187 @@
+/*
+ *  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 <arpa/inet.h>
+#include <fcntl.h>
+#include <stdint.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <sys/mman.h>
+#include <sys/stat.h>
+#include <sys/types.h>
+#include <unistd.h>
+
+#include <fenv.h>
+#include <limits>
+
+#include <complex>
+
+#include "gflags/gflags.h"
+#include "webrtc/base/checks.h"
+#include "webrtc/common_audio/real_fourier.h"
+#include "webrtc/modules/audio_processing/intelligibility/intelligibility_enhancer.h"
+#include "webrtc/modules/audio_processing/intelligibility/intelligibility_utils.h"
+#include "webrtc/system_wrappers/interface/critical_section_wrapper.h"
+#include "webrtc/system_wrappers/interface/scoped_ptr.h"
+
+const int16_t* in_ipcm;
+int16_t* out_ipcm;
+const int16_t* noise_ipcm;
+
+float* in_fpcm;
+float* out_fpcm;
+float* noise_fpcm;
+float* noise_cursor;
+float* clear_cursor;
+
+int samples;
+int fragment_size;
+
+using std::complex;
+using webrtc::RealFourier;
+using webrtc::IntelligibilityEnhancer;
+
+DEFINE_int32(clear_type, webrtc::intelligibility::VarianceArray::kStepInfinite,
+             "Variance algorithm for clear data.");
+DEFINE_double(clear_alpha, 0.9,
+              "Variance decay factor for clear data.");
+DEFINE_int32(clear_window, 475,
+             "Window size for windowed variance for clear data.");
+DEFINE_int32(sample_rate, 16000,
+             "Audio sample rate used in the input and output files.");
+DEFINE_int32(ana_rate, 800,
+             "Analysis rate; gains recalculated every N blocks.");
+DEFINE_int32(var_rate, 2,
+             "Variance clear rate; history is forgotten every N gain recalculations.");
+DEFINE_double(gain_limit, 1000.0, "Maximum gain change in one block.");
+
+DEFINE_bool(repeat, false, "Repeat input file ad nauseam.");
+
+DEFINE_string(clear_file, "speech.pcm", "Input file with clear speech.");
+DEFINE_string(noise_file, "noise.pcm", "Input file with noise data.");
+DEFINE_string(out_file, "proc_enhanced.pcm", "Enhanced output. Use '-' to "
+              "pipe through aplay internally.");
+
+// Write an Sun AU-formatted audio chunk into file descriptor |fd|. Can be used
+// to pipe the audio stream directly into aplay.
+void writeau(int fd) {
+  uint32_t thing;
+
+  write(fd, ".snd", 4);
+  thing = htonl(24);
+  write(fd, &thing, sizeof(thing));
+  thing = htonl(0xffffffff);
+  write(fd, &thing, sizeof(thing));
+  thing = htonl(3);
+  write(fd, &thing, sizeof(thing));
+  thing = htonl(FLAGS_sample_rate);
+  write(fd, &thing, sizeof(thing));
+  thing = htonl(1);
+  write(fd, &thing, sizeof(thing));
+
+  for (int i = 0; i < samples; ++i) {
+    out_ipcm[i] = htons(out_ipcm[i]);
+  }
+  write(fd, out_ipcm, sizeof(*out_ipcm) * samples);
+}
+
+int main(int argc, char* argv[]) {
+  google::SetUsageMessage("\n\nVariance algorithm types are:\n"
+                          "  0 - infinite/normal,\n"
+                          "  1 - exponentially decaying,\n"
+                          "  2 - rolling window.\n"
+                          "\nInput files must be little-endian 16-bit signed raw PCM.\n");
+  google::ParseCommandLineFlags(&argc, &argv, true);
+
+  const char* in_name = FLAGS_clear_file.c_str();
+  const char* out_name = FLAGS_out_file.c_str();
+  const char* noise_name = FLAGS_noise_file.c_str();
+  struct stat in_stat, noise_stat;
+  int in_fd, out_fd, noise_fd;
+  FILE* aplay_file = nullptr;
+
+  fragment_size = FLAGS_sample_rate / 100;
+
+  stat(in_name, &in_stat);
+  stat(noise_name, &noise_stat);
+  samples = in_stat.st_size / sizeof(*in_ipcm);
+
+  in_fd = open(in_name, O_RDONLY);
+  if (!strcmp(out_name, "-")) {
+    aplay_file = popen("aplay -t au", "w");
+    out_fd = fileno(aplay_file);
+  } else {
+    out_fd = open(out_name, O_WRONLY | O_CREAT | O_TRUNC,
+                  S_IRUSR | S_IWUSR | S_IRGRP | S_IWGRP | S_IROTH | S_IWOTH);
+  }
+  noise_fd = open(noise_name, O_RDONLY);
+
+  in_ipcm = static_cast<int16_t*>(mmap(nullptr, in_stat.st_size, PROT_READ,
+                                       MAP_PRIVATE, in_fd, 0));
+  noise_ipcm = static_cast<int16_t*>(mmap(nullptr, noise_stat.st_size,
+                                          PROT_READ, MAP_PRIVATE, noise_fd, 0));
+  out_ipcm = new int16_t[samples];
+  out_fpcm = new float[samples];
+  in_fpcm = new float[samples];
+  noise_fpcm = new float[samples];
+
+  for (int i = 0; i < samples; ++i) {
+    noise_fpcm[i] = noise_ipcm[i % (noise_stat.st_size / sizeof(*noise_ipcm))];
+  }
+
+  //feenableexcept(FE_INVALID | FE_OVERFLOW);
+  IntelligibilityEnhancer enh(2,
+                              FLAGS_sample_rate, 1,
+                              FLAGS_clear_type,
+                              static_cast<float>(FLAGS_clear_alpha),
+                              FLAGS_clear_window,
+                              FLAGS_ana_rate,
+                              FLAGS_var_rate,
+                              FLAGS_gain_limit);
+
+  // Slice the input into smaller chunks, as the APM would do, and feed them
+  // into the enhancer. Repeat indefinitely if FLAGS_repeat is set.
+  do {
+    noise_cursor = noise_fpcm;
+    clear_cursor = in_fpcm;
+    for (int i = 0; i < samples; ++i) {
+      in_fpcm[i] = in_ipcm[i];
+    }
+
+    for (int i = 0; i < samples; i += fragment_size) {
+      enh.ProcessCaptureAudio(&noise_cursor);
+      enh.ProcessRenderAudio(&clear_cursor);
+      clear_cursor += fragment_size;
+      noise_cursor += fragment_size;
+    }
+
+    for (int i = 0; i < samples; ++i) {
+      out_ipcm[i] = static_cast<float>(in_fpcm[i]);
+    }
+    if (!strcmp(out_name, "-")) {
+      writeau(out_fd);
+    } else {
+      write(out_fd, out_ipcm, samples * sizeof(*out_ipcm));
+    }
+  } while (FLAGS_repeat);
+
+  munmap(const_cast<int16_t*>(noise_ipcm), noise_stat.st_size);
+  munmap(const_cast<int16_t*>(in_ipcm), in_stat.st_size);
+  close(noise_fd);
+  if (aplay_file) {
+    pclose(aplay_file);
+  } else {
+    close(out_fd);
+  }
+  close(in_fd);
+
+  return 0;
+}
+
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 0000000..e6fc3fa
--- /dev/null
+++ 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
+
diff --git a/webrtc/modules/audio_processing/intelligibility/intelligibility_utils.h b/webrtc/modules/audio_processing/intelligibility/intelligibility_utils.h
new file mode 100644
index 0000000..550f293
--- /dev/null
+++ b/webrtc/modules/audio_processing/intelligibility/intelligibility_utils.h
@@ -0,0 +1,137 @@
+/*
+ *  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.
+ */
+
+#ifndef WEBRTC_MODULES_AUDIO_PROCESSING_INTELLIGIBILITY_INTELLIGIBILITY_UTILS_H_
+#define WEBRTC_MODULES_AUDIO_PROCESSING_INTELLIGIBILITY_INTELLIGIBILITY_UTILS_H_
+
+#include <complex>
+
+#include "webrtc/system_wrappers/interface/scoped_ptr.h"
+
+namespace webrtc {
+
+namespace intelligibility {
+
+// Internal helper for computing the variances of a stream of arrays.
+// The result is an array of variances per position: the i-th variance
+// is the variance of the stream of data on the i-th positions in the
+// input arrays.
+// There are four methods of computation:
+//  * kStepInfinite computes variances from the beginning onwards
+//  * kStepDecaying uses a recursive exponential decay formula with a
+//    settable forgetting factor
+//  * kStepWindowed computes variances within a moving window
+//  * kStepBlocked is similar to kStepWindowed, but history is kept
+//    as a rolling window of blocks: multiple input elements are used for
+//    one block and the history then consists of the variances of these blocks
+//    with the same effect as kStepWindowed, but less storage, so the window
+//    can be longer
+class VarianceArray {
+ public:
+  enum StepType {
+    kStepInfinite = 0,
+    kStepDecaying,
+    kStepWindowed,
+    kStepBlocked
+  };
+
+  // Construct an instance for the given input array length (|freqs|) and
+  // computation algorithm (|type|), with the appropriate parameters.
+  // |window_size| is the number of samples for kStepWindowed and
+  // the number of blocks for kStepBlocked. |decay| is the forgetting factor
+  // for kStepDecaying.
+  VarianceArray(int freqs, StepType type, int window_size, float decay);
+
+  // Add a new data point to the series and compute the new variances.
+  // TODO(bercic) |skip_fudge| is a flag for kStepWindowed and kStepDecaying,
+  // whether they should skip adding some small dummy values to the input
+  // to prevent problems with all-zero inputs. Can probably be removed.
+  void Step(const std::complex<float>* data, bool skip_fudge = false) {
+    (this->*step_func_)(data, skip_fudge);
+  }
+  // Reset variances to zero and forget all history.
+  void Clear();
+  // Scale the input data by |scale|. Effectively multiply variances
+  // by |scale^2|.
+  void ApplyScale(float scale);
+
+  // The current set of variances.
+  const float* variance() const {
+    return variance_.get();
+  }
+
+  // The mean value of the current set of variances.
+  float array_mean() const {
+    return array_mean_;
+  }
+
+ private:
+  void InfiniteStep(const std::complex<float>* data, bool dummy);
+  void DecayStep(const std::complex<float>* data, bool dummy);
+  void WindowedStep(const std::complex<float>* data, bool dummy);
+  void BlockedStep(const std::complex<float>* data, bool dummy);
+
+  // The current average X and X^2.
+  scoped_ptr<std::complex<float>[]> running_mean_;
+  scoped_ptr<std::complex<float>[]> running_mean_sq_;
+
+  // Average X and X^2 for the current block in kStepBlocked.
+  scoped_ptr<std::complex<float>[]> sub_running_mean_;
+  scoped_ptr<std::complex<float>[]> sub_running_mean_sq_;
+
+  // Sample history for the rolling window in kStepWindowed and block-wise
+  // histories for kStepBlocked.
+  scoped_ptr<scoped_ptr<std::complex<float>[]>[]> history_;
+  scoped_ptr<scoped_ptr<std::complex<float>[]>[]> subhistory_;
+  scoped_ptr<scoped_ptr<std::complex<float>[]>[]> subhistory_sq_;
+
+  // The current set of variances and sums for Welford's algorithm.
+  scoped_ptr<float[]> variance_;
+  scoped_ptr<float[]> conj_sum_;
+
+  const int freqs_;
+  const int window_size_;
+  const float decay_;
+  int history_cursor_;
+  int count_;
+  float array_mean_;
+  void (VarianceArray::*step_func_)(const std::complex<float>*, bool);
+};
+
+// Helper class for smoothing gain changes. On each applicatiion step, the
+// currently used gains are changed towards a set of settable target gains,
+// constrained by a limit on the magnitude of the changes.
+class GainApplier {
+ public:
+  GainApplier(int freqs, float change_limit);
+
+  // Copy |in_block| to |out_block|, multiplied by the current set of gains,
+  // and step the current set of gains towards the target set.
+  void Apply(const std::complex<float>* in_block,
+             std::complex<float>* out_block);
+
+  // Return the current target gain set. Modify this array to set the targets.
+  float* target() const {
+    return target_.get();
+  }
+
+ private:
+  const int freqs_;
+  const float change_limit_;
+  scoped_ptr<float[]> target_;
+  scoped_ptr<float[]> current_;
+};
+
+}  // namespace intelligibility
+
+}  // namespace webrtc
+
+#endif  // WEBRTC_MODULES_AUDIO_PROCESSING_INTELLIGIBILITY_INTELLIGIBILITY_UTILS_H_
+