Add new variance update option and unittests for intelligibility

webrtc/modules/audio_processing/intelligibility/intelligibility_utils.h

 /*
  *  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.
  */
 
 //
 //  Specifies helper classes for intelligibility enhancement.
 //
 
 #ifndef WEBRTC_MODULES_AUDIO_PROCESSING_INTELLIGIBILITY_INTELLIGIBILITY_UTILS_H_
 #define WEBRTC_MODULES_AUDIO_PROCESSING_INTELLIGIBILITY_INTELLIGIBILITY_UTILS_H_
 
 #include <complex>
 
 #include "webrtc/base/scoped_ptr.h"
 
 namespace webrtc {
 
 namespace intelligibility {
 
+// Return |current| changed towards |target|, with the change being at most
+// |limit|.
+float UpdateFactor(float target, float current, float limit);
+
+// std::isfinite for complex numbers.
+bool cplxfinite(std::complex<float> c);
+
+// std::isnormal for complex numbers.
+bool cplxnormal(std::complex<float> c);
+
+// 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.
+std::complex<float> zerofudge(std::complex<float> c);
+
+// Incremental mean computation. Return the mean of the series with the
+// mean |mean| with added |data|.
+std::complex<float> NewMean(std::complex<float> mean,
+                            std::complex<float> data,
+                            int count);
+
+// Updates |mean| with added |data|;
+void AddToMean(std::complex<float> data, int count, std::complex<float>* mean);
+
 // 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
+    kStepBlocked,
+    kStepBlockBasedMovingAverage
   };
 
   // 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.
@@ skipping 13 matching lines @@
   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);
+  void BlockBasedMovingAverage(const std::complex<float>* data, bool dummy);
 
   // TODO(ekmeyerson): Switch the following running means
   // and histories from rtc::scoped_ptr to std::vector.
 
   // The current average X and X^2.
   rtc::scoped_ptr<std::complex<float>[]> running_mean_;
   rtc::scoped_ptr<std::complex<float>[]> running_mean_sq_;
 
   // Average X and X^2 for the current block in kStepBlocked.
   rtc::scoped_ptr<std::complex<float>[]> sub_running_mean_;
   rtc::scoped_ptr<std::complex<float>[]> sub_running_mean_sq_;
 
   // Sample history for the rolling window in kStepWindowed and block-wise
   // histories for kStepBlocked.
   rtc::scoped_ptr<rtc::scoped_ptr<std::complex<float>[]>[]> history_;
   rtc::scoped_ptr<rtc::scoped_ptr<std::complex<float>[]>[]> subhistory_;
   rtc::scoped_ptr<rtc::scoped_ptr<std::complex<float>[]>[]> subhistory_sq_;
 
   // The current set of variances and sums for Welford's algorithm.
   rtc::scoped_ptr<float[]> variance_;
   rtc::scoped_ptr<float[]> conj_sum_;
 
   const int freqs_;
   const int window_size_;
   const float decay_;
   int history_cursor_;
   int count_;
   float array_mean_;
+  bool buffer_full_;
   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);
 
@@ skipping 10 matching lines @@
   const float change_limit_;
   rtc::scoped_ptr<float[]> target_;
   rtc::scoped_ptr<float[]> current_;
 };
 
 }  // namespace intelligibility
 
 }  // namespace webrtc
 
 #endif  // WEBRTC_MODULES_AUDIO_PROCESSING_INTELLIGIBILITY_INTELLIGIBILITY_UTILS_H_