Fix and simplify the power estimation in the IntelligibilityEnhancer

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 {
+// Internal helper for computing the power of a stream of arrays.
+// The result is an array of power per position: the i-th power is the power of
+// the stream of data on the i-th positions in the input arrays.
+class PowerEstimator {
+ public:
+  // Construct an instance for the given input array length (|freqs|), with the
+  // appropriate parameters. |decay| is the forgetting factor.
+  PowerEstimator(size_t freqs, float decay);
 
-// Return |current| changed towards |target|, with the change being at most
-// |limit|.
-float UpdateFactor(float target, float current, float limit);
+  // Add a new data point to the series.
+  void Step(const std::complex<float>* data);
 
-// 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,
-                            size_t count);
-
-// Updates |mean| with added |data|;
-void AddToMean(std::complex<float> data,
-               size_t 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,
-    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(size_t freqs, StepType type, size_t 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_; }
+  // The current power array.
+  const float* Power();
 
  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_;
+  // The current magnitude array.
+  rtc::scoped_ptr<float[]> magnitude_;
+  // The current power array.
+  rtc::scoped_ptr<float[]> power_;
 
   const size_t num_freqs_;
-  const size_t window_size_;
   const float decay_;
-  size_t history_cursor_;
-  size_t 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
+// Helper class for smoothing gain changes. On each application 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(size_t 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 size_t num_freqs_;
   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_