Update a ton of audio code to use size_t more correctly and in general reduce

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.
  */
 
@@ skipping 18 matching lines @@
 
 // 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);
+                            size_t count);
 
 // Updates |mean| with added |data|;
-void AddToMean(std::complex<float> data, int count, std::complex<float>* mean);
+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
@@ skipping 10 matching lines @@
     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(int freqs, StepType type, int window_size, float decay);
+  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();
@@ skipping 28 matching lines @@
   // 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 num_freqs_;
-  const int window_size_;
+  const size_t num_freqs_;
+  const size_t window_size_;
   const float decay_;
-  int history_cursor_;
-  int count_;
+  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
 // 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);
+  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 int num_freqs_;
+  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_