refl_05.cc

这是一个从音频信号里提取特征参量的程序

// file: $isip/class/algo/Reflection/refl_05.cc
// version: $Id: refl_05.cc,v 1.21 2002/02/28 03:21:55 zheng Exp $
//

// isip include files
//
#include "Reflection.h"

// method: apply
//
// arguments:
//  Vector<AlgorithmData>& output: (output) output data
//  const Vector< CircularBuffer<AlgorithmData> >& input: (input) input data
//
// return: a boolean value indicating status
//
// this method calls the appropriate computation methods
//
boolean Reflection::apply(Vector<AlgorithmData>& output_a,
			  const Vector< CircularBuffer<AlgorithmData> >&
			  input_a) {

  // determine the number of input channels and force the output to be
  // that number
  //
  long len = input_a.length();
  output_a.setLength(len);
  boolean res = true;

  // start the debugging output
  //
  displayStart(this);
  
  // loop over the channels and call the compute method
  //
  for (long c = 0; c < len; c++) {

    // display the channel number
    //
    displayChannel(c);
    
    // branch on input coefficient type - covariance takes matrix as input
    //
    if (input_a(c)(0).getCoefType() == AlgorithmData::COVARIANCE) {
      
      // call AlgorithmData::makeVectorFloat to force the output vector for
      // this channel to be a VectorFloat.
      // call AlgorithmData::getMatrixFloat on the input for this channel
      // to check that the input is already a MatrixFloat and return
      // that matrix.
      //
      res &= compute(output_a(c).makeVectorFloat(),
		     input_a(c)(0).getMatrixFloat(),
		     input_a(c)(0).getCoefType(),
		     c);
    }

    // branch on input coefficient type - others take vector as input
    //
    else {
      // call AlgorithmData::makeVectorFloat to force the output vector for
      // this channel to be a VectorFloat, call AlgorithmData::getVectorFloat
      // on the input for this channel to check that the input is
      // already a VectorFloat and return that vector.
      //
      res &= compute(output_a(c).makeVectorFloat(),
		     input_a(c)(0).getVectorFloat(),
		     input_a(c)(0).getCoefType(),
		     c);
    }

    // set the coefficient type for the output
    //            
    output_a(c).setCoefType(AlgorithmData::REFLECTION);
  }

  // finish the debugging output
  //
  displayFinish(this);
  
  // exit gracefully
  //
  return res;
}

// method: compute
//
// arguments:
//  VectorFloat& coeff: (output) output reflection coefficients
//  const VectorFloat& input: (input) input data
//  AlgorithmData::COEF_TYPE input_coef_type: (input) type of input
//  long index: (input) channel number
//
// return: a boolean value indicating status
//
// this method computes the reflection coefficents for given input data
//
boolean Reflection::compute(VectorFloat& coeff_a, const VectorFloat& input_a,
			    AlgorithmData::COEF_TYPE input_coef_type_a,
			    long index_a) {
  
  // declare local variable
  //
  Float temp_energy;
  
  // invoke the appropriate compute method and exit gracefully
  //
  return compute(coeff_a, temp_energy, input_a, input_coef_type_a, index_a);
}

// method: compute
//
// arguments:
//  VectorFloat& coeff: (output) predictor or reflection coefficents
//  const MatrixFloat& input: (input) input data (covariance matrix)
//  AlgorithmData::COEF_TYPE input_coef_type: (input) type of input
//  long index: (input) channel number
//
// return: a boolean value indicating status
//
// this method computes the reflection coefficents from input covariance matrix
//
boolean Reflection::compute(VectorFloat& coeff_a, const MatrixFloat& input_a,
			    AlgorithmData::COEF_TYPE input_coef_type_a,
			    long index_a) {

  // declare local variable
  //
  Float temp_energy;
  
  // invoke the appropriate compute method and exit gracefully
  //
  return compute(coeff_a, temp_energy, input_a, input_coef_type_a);
}

// method: compute
//
// arguments:
//  VectorFloat& coeff: (output) output reflection coefficients
//  Float& err_energy: (output) error energy  
//  const VectorFloat& input: (input) input data
//  AlgorithmData::COEF_TYPE input_coef_type: (input) type of input
//  long index: (input) channel number
//
// return: a boolean value indicating status
//
// this method computes the reflection coefficents as well as the error energy
//
boolean Reflection::compute(VectorFloat& coeff_a, Float& err_energy_a,
			    const VectorFloat& input_a,
			    AlgorithmData::COEF_TYPE input_coef_type_a,
			    long index_a) {
  
  // declare local variable
  //
  boolean status = false;
  
  // algorithm: AUTOCORRELATION
  //
  if (algorithm_d == AUTOCORRELATION) {

    // implementation: DURBIN
    //
    if (implementation_d == DURBIN) {
      status = computeAutoDurbin(coeff_a, err_energy_a, input_a);
    }

    // implementation: LEROUX_GUEGUEN
    //
    else if (implementation_d == LEROUX_GUEGUEN) {
      status = computeAutoLeroux(coeff_a, err_energy_a, input_a);
    }

    // error: unknown implementation
    //
    else {
      return Error::handle(name(), L"compute", ERR_UNKIMP,
			   __FILE__, __LINE__);
    }
  }
  
  // algorithm: LATTICE
  //
  else if (algorithm_d == LATTICE) {

    // implementation: BURG
    //
    if (implementation_d == BURG) {
      status = computeLatticeBurg(coeff_a, err_energy_a, input_a);
    }

    // error: unknown implementation
    //
    else {
      return Error::handle(name(), L"compute", ERR_UNKIMP,
			   __FILE__, __LINE__);
    }
  }
  
  // algorithm: PREDICTION
  //
  else if (algorithm_d == PREDICTION) {

    // implementation: STEP_UP
    //
    if (implementation_d == STEP_UP) {
      status = computePredictionStepUp(coeff_a, input_a);
    }

    // error: unknown implementation
    //
    else {
      return Error::handle(name(), L"compute", ERR_UNKIMP,
			   __FILE__, __LINE__);
    }
  }
  
  // algorithm: LOG_AREA_RATIO
  //
  else if (algorithm_d == LOG_AREA_RATIO) {

    // implementation: KELLY_LOCHBAUM
    //
    if (implementation_d == KELLY_LOCHBAUM) {
      status = computeLogAreaKellyLochbaum(coeff_a, input_a);
    }

    // error: unknown implementation
    //
    else {
      return Error::handle(name(), L"compute", ERR_UNKIMP,
			   __FILE__, __LINE__);
    }
  }
  
  // error: unknown algorithm
  //
  else {
    status = Error::handle(name(), L"compute", ERR_UNKALG,
			   __FILE__, __LINE__);
  }

  // possibly display the data
  //
  display(coeff_a, input_a, name());
  
  // exit gracefully
  //
  return status;
}

// method: compute
//
// arguments:
//  VectorFloat& coeff: (output) predictor or reflection coefficents
//  Float& err_energy: (output) error energy  
//  const MatrixFloat& input: (input) input data (covariance matrix)
//  AlgorithmData::COEF_TYPE input_coef_type: (input) type of input
//  long index: (input) channel number
//
// return: a boolean value indicating status
//
// this method computes the reflection coefficents from input covariance matrix
// as well as the error energy
//
boolean Reflection::compute(VectorFloat& coeff_a, Float& err_energy_a,
			    const MatrixFloat& input_a,
			    AlgorithmData::COEF_TYPE input_coef_type_a,
			    long index_a) {

  // declare local variable
  //
  boolean status = false;
  
  // algorithm: COVARIANCE
  //
  if (algorithm_d == COVARIANCE) {

    // implementation: CHOLESKY
    //
    if (implementation_d == CHOLESKY) {
      status = computeCovarCholesky(coeff_a, err_energy_a, input_a);
    }

    // error: unknown implementation
    //
    else {
      return Error::handle(name(), L"compute", ERR_UNKIMP,
			   __FILE__, __LINE__);
    }
  }
  
  // error: unknown algorithm
  //
  else {
    status = Error::handle(name(), L"compute", ERR_UNKALG,
			   __FILE__, __LINE__);
  }

  // possibly display the data
  //
  display(coeff_a, input_a, name());
  
  // exit gracefully
  //
  return status;
}

// method: computeAutoDurbin
//
// arguments:
//  VectorFloat& output: (output) reflection coefficients
//  Float& err_energy: (output) error energy  
//  const VectorFloat& input: (input) autocorrelation coefficients
//
// return: a boolean value indicating status
//
// this method computes the reflection coefficients from the
// autocorrelation coefficents. it also outputs the error energy.
//
// Reference:
//  J.D. Markel and A.H. Gray, "Linear Prediction of Speech,"
//  Springer-Verlag Berlin Heidelberg, New York, USA, pp. 217-219,
//  1980.
//
boolean Reflection::computeAutoDurbin(VectorFloat& output_a,
				      Float& err_energy_a,
				      const VectorFloat& input_a) {

  // declare local variables
  //
  VectorFloat pred_coef;
  
  // apply dynamic range threshold
  //
  float old_auto_0 = input_a(0);
  if (dyn_range_d < (float)0) {
    double scale_factor =
      pow(10.0, (float)dyn_range_d / Integral::DB_POW_SCALE_FACTOR);
    const_cast<VectorFloat&>(input_a)(0) *= (1 + scale_factor);
  }  
  else {
    return Error::handle(name(), L"computeAutoDurbin", ERR_DYNRANGE,
			 __FILE__, __LINE__);
  }  
  
  // declare local variables
  //
  long i, j;                                 // loop counters
  long lp_order = input_a.length() - 1;       // analysis order
  long j_bckwd;                              // a backwards index
  long middle_index;                         // inner loop limit
  double sum;                                // accumulator
  double rc_reg;                             // register

  // create space
  //
  if (!pred_coef.setLength(lp_order + 1)) {
    return Error::handle(name(), L"computeAutoDurbin", ERR,
			 __FILE__, __LINE__, Error::WARNING);
  }

  if (!output_a.setLength(lp_order)) {
    return Error::handle(name(), L"computeAutoDurbin", ERR,
			 __FILE__, __LINE__, Error::WARNING);
  }

  // initialization
  //
  pred_coef(0) = 1.0;
  err_energy_a = input_a(0);

  // if the energy of the signal is zero we set all the predictor
  // coefficients to zero and exit
  //
  if (err_energy_a == (float)0) {
    for (int i = 0; i < output_a.length(); i++) {
      output_a(i) = 0;
    }
    return true;
  }
  
  // do the first step manually
  //
  sum = -(double)input_a(1) / err_energy_a;
  output_a(0) = sum;
  pred_coef(1) = sum;
  err_energy_a *= (1 - sum * sum);

  // recursion
  //
  for (i = 2; i <= lp_order; i++) {

    // compute the next reflection coefficient
    //
    sum = 0;
    for (j = 1; j < i; j++) {
      sum += pred_coef(j) * input_a(i - j);
    }

    // check for divide by zero error
    //
    if (err_energy_a == (float)0) {
      return Error::handle(name(), L"computeAutoDurbin", Error::ARG, __FILE__, __LINE__);
    }    
    rc_reg = - ((float)input_a(i) + sum) / err_energy_a;

    // compute the new prediction coefficients
    //  note: the algorithm used here minimizes the storage.
    //
    // Reference:
    //  J.D. Markel and A.H. Gray, "Linear Prediction of Speech,"
    //  Springer-Verlag Berlin Heidelberg, New York, USA, pp. 219,
    //  1980.
    //
    output_a(i - 1) = rc_reg;
    pred_coef(i) = rc_reg;
    j_bckwd = i - 1;
    middle_index = i / 2;
    for (j = 1; j <= middle_index; j++) {
      sum = pred_coef(j_bckwd) + (float)rc_reg* pred_coef(j); 
      pred_coef(j) = pred_coef(j) + (float)rc_reg * pred_coef(i - j);
      pred_coef(j_bckwd) = sum;
      j_bckwd--;
    }

    // compute new error
    //
    err_energy_a *= 1.0 - rc_reg * rc_reg;
  }
  
  // resume the input_a(0)
  //
  const_cast<VectorFloat&>(input_a)(0) = old_auto_0;
  
  // exit gracefully
  //
  return true;
}

// method: computeAutoLeroux
//
// arguments:
//  VectorFloat& output: (output) reflection coefficients
//  Float& err_energy: (output) error energy  
//  const VectorFloat& input: (input) input data
//
// return: a boolean value indicating status
//
// this method computes the reflection coefficients from the
// autocorrelation coefficents. it also outputs the error energy.
//
// Reference:
//  J.D. Markel and A.H. Gray, "Linear Prediction of Speech,"
//  Springer-Verlag Berlin Heidelberg, New York, USA, pp. 217-219,
//  1980.
//
boolean Reflection::computeAutoLeroux(VectorFloat& output_a,
				      Float& err_energy_a,
				      const VectorFloat& input_a) {

  return Error::handle(name(), L"computeAutoLeroux", ERR_UNKIMP,
		       __FILE__, __LINE__);
}

// method: computeCovarCholesky
//
// arguments:
//  VectorFloat& output: (output) reflection coefficients
//  Float& err_energy: (output) error energy  
//  const MatrixFloat& cor: (input) covariance coefficients
//
// return: a boolean value indicating status
//
// this method computes the reflection coefficients using the
// covariance analysis method. it also outputs the error energy.
//
// Reference:
//  J.D. Markel and A.H. Gray, "Linear Prediction of Speech,"
//  Springer-Verlag Berlin Heidelberg, New York, USA, pp. 51-55, 1980.
//
boolean Reflection::computeCovarCholesky(VectorFloat& output_a,
					 Float& err_energy_a,
					 const MatrixFloat& cor_a) {

  // declare local variable
  //
  VectorFloat pred_coef;