annotate src/Modules/SNR/ModuleNoise.cc @ 611:0fbaf443ec82

Carfac C++ revision 3, indluding more style improvements. The output structs are now classes again, and have separate storage methods for each output structure along with flags in the Run and RunSegment methods to allow for only storing NAPs if desired.

// Copyright 2010, Thomas Walters
//
// AIM-C: A C++ implementation of the Auditory Image Model
// http://www.acousticscale.org/AIMC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

/*!
 * \author Thomas Walters <[e-mail]>
 * \date created 2010/02/24
 * \version \$Id$
 */

// Use the boost RNGs to generate Gaussian noise
#include <boost/random.hpp>
#include <math.h>

#include "Modules/SNR/ModuleNoise.h"

namespace aimc {
ModuleNoise::ModuleNoise(Parameters *params) :
    Module(params),
    gaussian_variate_(boost::mt19937(),
                      boost::normal_distribution<float>(0.0f, 1.0f)) {
  module_description_ = "Adds noise to a signal";
  module_identifier_ = "noise";
  module_type_ = "snr";
  module_version_ = "$Id$";

  pink_ = parameters_->DefaultBool("noise.pink", true);
  // Noise level relative to unit-variance Gaussian noise (ie. 0dB will give a
  // noise with an RMS level of 1.0)
  float snr_db = parameters_->DefaultFloat("noise.level_db", 0.0f);
  multiplier_ = pow(10.0f, snr_db / 20.0f);
}

ModuleNoise::~ModuleNoise() {
}

bool ModuleNoise::InitializeInternal(const SignalBank &input) {
  // Copy the parameters of the input signal bank into internal variables, so
  // that they can be checked later.
  sample_rate_ = input.sample_rate();
  buffer_length_ = input.buffer_length();
  channel_count_ = input.channel_count();

  output_.Initialize(input);
  ResetInternal();
  return true;
}

void ModuleNoise::ResetInternal() {
  s0_ = 0.0f;
  s1_ = 0.0f;
  s2_ = 0.0f;
}

void ModuleNoise::Process(const SignalBank &input) {
  // Check to see if the module has been initialized. If not, processing
  // should not continue.
  if (!initialized_) {
    LOG_ERROR(_T("Module %s not initialized."), module_identifier_.c_str());
    return;
  }

  // Check that ths input this time is the same as the input passed to
  // Initialize()
  if (buffer_length_ != input.buffer_length()
      || channel_count_ != input.channel_count()) {
    LOG_ERROR(_T("Mismatch between input to Initialize() and input to "
                 "Process() in module %s."), module_identifier_.c_str());
    return;
  }

  for (int c = 0; c < input.channel_count(); ++c) {
    for (int i = 0; i < input.buffer_length(); ++i) {
      float s = input[c][i];
      float n =  gaussian_variate_();
      if (pink_) {
        // Pink noise filter coefficients from 
        // ccrma.stanford.edu/~jos/sasp/Example_Synthesis_1_F_Noise.html
        // Smith, Julius O. Spectral Audio Signal Processing, October 2008
        // Draft, http://ccrma.stanford.edu/~jos/sasp/, online book, 
        // accessed 2010-02-27.
        float f = 0.049922035 * n + s0_;
        s0_ = -0.095993537 * n - (-2.494956002 * f) + s1_;
        s1_ = 0.050612699 * n - (2.017265875 * f) + s2_;
        s2_ = -0.004408786 * n - (-0.522189400 * f);
        n = f;
      }
      s += multiplier_ * n;
      output_.set_sample(c, i, s);
    }
  }
  PushOutput();
}
}  // namespace aimc