diff trunk/src/Modules/Features/ModuleGaussians.cc @ 268:e14c70d1b171

- Initial add of support code and modules. Not everything is working yet.

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/trunk/src/Modules/Features/ModuleGaussians.cc	Fri Feb 12 12:31:23 2010 +0000
@@ -0,0 +1,282 @@
+// Copyright 2008-2010, Thomas Walters
+//
+// AIM-C: A C++ implementation of the Auditory Image Model
+// http://www.acousticscale.org/AIMC
+//
+// This program is free software: you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation, either version 3 of the License, or
+// (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU General Public License
+// along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+/*! \file
+ *  \brief Gaussian features - based on MATLAB code by Christian Feldbauer
+ */
+
+/*!
+ * \author Tom Walters <[e-mail]>
+ * \date created 2008/06/23
+ * \version \$Id: ModuleGaussians.cc 2 2010-02-02 12:59:50Z tcw $
+ */
+
+#include <math.h>
+
+#include "Modules/Features/ModuleGaussians.h"
+#include "Support/Common.h"
+
+namespace aimc {
+ModuleGaussians::ModuleGaussians(Parameters *pParam)
+: Module(pParam) {
+  // Set module metadata
+  module_description_ = "Gaussian Fitting to SSI profile";
+  module_identifier_ = "gaussians"; // unique identifier for the module
+  module_type_ = "features";
+  module_version_ = "$Id: ModuleGaussians.cc 2 2010-02-02 12:59:50Z tcw $";
+
+  parameters_->SetDefault("features.gaussians.ncomp", "4");
+  m_iParamNComp = parameters_->GetInt("features.gaussians.ncomp");
+
+  parameters_->SetDefault("features.gaussians.var", "115.0");
+  m_fParamVar = parameters_->GetFloat("features.gaussians.var");
+
+  parameters_->SetDefault("features.gaussians.posterior_exp", "6.0");
+  m_fParamPosteriorExp =
+    parameters_->GetFloat("features.gaussians.posterior_exp");
+
+  parameters_->SetDefault("features.gaussians.maxit", "250");
+  m_iParamMaxIt = parameters_->GetInt("features.gaussians.maxit");
+
+  parameters_->SetDefault("features.gaussians.priors_converged", "1e-7");
+  m_fParamPriorsConverged =
+    parameters_->GetInt("features.gaussians.priors_converged");
+}
+
+ModuleGaussians::~ModuleGaussians() {
+}
+
+bool ModuleGaussians::InitializeInternal(const SignalBank &input) {
+  m_pA.resize(m_iParamNComp, 0.0f);
+  m_pMu.resize(m_iParamNComp, 0.0f);
+
+  // Assuming the number of channels is greater than twice the number of
+  // Gaussian components, this is ok
+  if (input.channel_count() >= 2 * m_iParamNComp) {
+    output_.Initialize(1, m_iParamNComp, input.sample_rate());
+  } else {
+    LOG_ERROR(_T("Too few channels in filterbank to produce sensible "
+                 "Gaussian features. Either increase the number of filterbank"
+                 " channels, or decrease the number of Gaussian components"));
+    return false;
+  }
+
+  m_iNumChannels = input.channel_count();
+  m_pSpectralProfile.resize(m_iNumChannels, 0.0f);
+
+  return true;
+}
+
+void ModuleGaussians::Reset() {
+  m_pSpectralProfile.clear();
+  m_pSpectralProfile.resize(m_iNumChannels, 0.0f);
+}
+
+void ModuleGaussians::Process(const SignalBank &input) {
+  int iAudCh = 0;
+
+  // Calculate spectral profile
+  for (int iChannel = 0;
+       iChannel < input.channel_count();
+       ++iChannel) {
+    m_pSpectralProfile[iChannel] = 0.0f;
+    for (int iSample = 0;
+         iSample < input.buffer_length();
+         ++iSample) {
+      m_pSpectralProfile[iChannel] += input[iChannel][iSample];
+    }
+  }
+
+  for (int iChannel = 0;
+       iChannel < input.channel_count();
+       ++iChannel) {
+    m_pSpectralProfile[iChannel] = pow(m_pSpectralProfile[iChannel], 0.8);
+  }
+
+  float spectral_profile_sum = 0.0f;
+  for (int i = 0; i < input.channel_count(); ++i) {
+    spectral_profile_sum += m_pSpectralProfile[i];
+  }
+
+  RubberGMMCore(2, true);
+
+  float fMean1 = m_pMu[0];
+  float fMean2 = m_pMu[1];
+
+  float fA1 = 0.05 * m_pA[0];
+  float fA2 = 1.0 - 0.25 * m_pA[1];
+
+  float fGradient = (fMean2 - fMean1) / (fA2 - fA1);
+  float fIntercept = fMean2 - fGradient * fA2;
+
+  for (int i = 0; i < m_iParamNComp; ++i) {
+    m_pMu[i] = ((float)i / (float)m_iParamNComp - 1.0f)
+                  * -fGradient + fIntercept;
+  }
+
+  for (int i = 0; i < m_iParamNComp; ++i) {
+    m_pA[i] = 1.0f / (float)m_iParamNComp;
+  }
+
+  RubberGMMCore(m_iParamNComp, false);
+
+  for (int i = 0; i < m_iParamNComp - 1; ++i) {
+    if (!isnan(m_pA[i])) {
+      output_.set_sample(i, 0, m_pA[i]);
+    } else {
+      output_.set_sample(i, 0, 0.0f);
+    }
+  }
+  /*for (int i = m_iParamNComp; i < m_iParamNComp * 2; ++i) {
+    m_pOutputData->getSignal(i)->setSample(iAudCh, 0, m_pMu[i-m_iParamNComp]);
+  }*/
+  double logsum = log(spectral_profile_sum);
+  if (!isinf(logsum)) {
+    output_.set_sample(m_iParamNComp - 1, 0, logsum);
+  } else {
+    output_.set_sample(m_iParamNComp - 1, 0, -1000.0);
+  }
+  PushOutput();
+}
+
+bool ModuleGaussians::RubberGMMCore(int iNComponents, bool bDoInit) {
+  int iSizeX = m_iNumChannels;
+
+  // Normalise the spectral profile
+  float fSpectralProfileTotal = 0.0f;
+  for (int iCount = 0; iCount < iSizeX; iCount++) {
+    fSpectralProfileTotal += m_pSpectralProfile[iCount];
+  }
+  for (int iCount = 0; iCount < iSizeX; iCount++) {
+    m_pSpectralProfile[iCount] /= fSpectralProfileTotal;
+  }
+
+  if (bDoInit) {
+    // Uniformly spaced components
+    float dd = (iSizeX - 1.0f) / iNComponents;
+    for (int i = 0; i < iNComponents; i++) {
+      m_pMu[i] = dd / 2.0f + (i * dd);
+      m_pA[i] = 1.0f / iNComponents;
+    }
+  }
+
+  vector<float> pA_old;
+  pA_old.resize(iNComponents);
+  vector<float> pP_mod_X;
+  pP_mod_X.resize(iSizeX);
+  vector<float> pP_comp;
+  pP_comp.resize(iSizeX * iNComponents);
+
+  for (int iIteration = 0; iIteration < m_iParamMaxIt; iIteration++) {
+    // (re)calculate posteriors (component probability given observation)
+    // denominator: the model density at all observation points X
+    for (int i = 0; i < iSizeX; ++i) {
+      pP_mod_X[i] = 0.0f;
+    }
+
+    for (int i = 0; i < iNComponents; i++) {
+      for (int iCount = 0; iCount < iSizeX; iCount++) {
+        pP_mod_X[iCount] += 1.0f / sqrt(2.0f * M_PI * m_fParamVar)
+                            * exp((-0.5f) * pow(((float)iCount-m_pMu[i]), 2)
+                                  / m_fParamVar) * m_pA[i];
+      }
+    }
+
+    for (int i = 0; i < iSizeX * iNComponents; ++i) {
+      pP_comp[i] = 0.0f;
+    }
+
+    for (int i = 0; i < iNComponents; i++) {
+      for (int iCount = 0; iCount < iSizeX; iCount++) {
+        pP_comp[iCount + i * iSizeX] =
+          1.0f / sqrt(2.0f * M_PI * m_fParamVar)
+          * exp((-0.5f) * pow(((float)iCount - m_pMu[i]), 2) / m_fParamVar);
+        pP_comp[iCount + i * iSizeX] =
+          pP_comp[iCount + i * iSizeX] * m_pA[i] / pP_mod_X[iCount];
+      }
+    }
+
+    for (int iCount = 0; iCount < iSizeX; ++iCount) {
+      float fSum = 0.0f;
+      for (int i = 0; i < iNComponents; ++i) {
+        pP_comp[iCount+i*iSizeX] = pow(pP_comp[iCount + i * iSizeX],
+                                       m_fParamPosteriorExp); // expansion
+        fSum += pP_comp[iCount+i*iSizeX];
+      }
+      for (int i = 0; i < iNComponents; ++i)
+        pP_comp[iCount+i*iSizeX] = pP_comp[iCount + i * iSizeX] / fSum;
+        // renormalisation
+    }
+
+    for (int i = 0; i < iNComponents; ++i) {
+      pA_old[i] = m_pA[i];
+      m_pA[i] = 0.0f;
+      for (int iCount = 0; iCount < iSizeX; ++iCount) {
+        m_pA[i] += pP_comp[iCount + i * iSizeX] * m_pSpectralProfile[iCount];
+      }
+    }
+
+    // finish when already converged
+    float fPrdist = 0.0f;
+    for (int i = 0; i < iNComponents; ++i) {
+      fPrdist += pow((m_pA[i] - pA_old[i]), 2);
+    }
+    fPrdist /= iNComponents;
+
+    if (fPrdist < m_fParamPriorsConverged) {
+      LOG_INFO("Converged!");
+      break;
+    }
+
+    // update means (positions)
+    for (int i = 0 ; i < iNComponents; ++i) {
+      float mu_old = m_pMu[i];
+      if (m_pA[i] > 0.0f) {
+        m_pMu[i] = 0.0f;
+        for (int iCount = 0; iCount < iSizeX; ++iCount) {
+          m_pMu[i] += m_pSpectralProfile[iCount]
+                      * pP_comp[iCount + i * iSizeX] * (float)iCount;
+        }
+        m_pMu[i] /= m_pA[i];
+        if (isnan(m_pMu[i])) {
+          m_pMu[i] = mu_old;
+        }
+      }
+    }
+  } // loop over iterations
+
+  // Ensure they are sorted, using a really simple bubblesort
+  bool bSorted = false;
+  while (!bSorted) {
+    bSorted = true;
+    for (int i = 0; i < iNComponents - 1; ++i) {
+      if (m_pMu[i] > m_pMu[i + 1]) {
+        float fTemp = m_pMu[i];
+        m_pMu[i] = m_pMu[i + 1];
+        m_pMu[i + 1] = fTemp;
+        fTemp = m_pA[i];
+        m_pA[i] = m_pA[i + 1];
+        m_pA[i + 1] = fTemp;
+        bSorted = false;
+      }
+    }
+  }
+  return true;
+}
+}  //namespace aimc
+