pred_02.cc

这是一个从音频信号里提取特征参量的程序

// file: $isip/class/algo/Prediction/pred_02.cc
// version: $Id: pred_02.cc,v 1.12 2002/03/05 20:15:10 zheng Exp $
//

// isip include files
//
#include <Console.h>
#include "Prediction.h"
#include "LogAreaRatio.h"

// method: diagnose
//
// arguments:
//  Integral::DEBUG level: (input) debug level for diagnostics
//
// return: a boolean value indicating status
//
boolean Prediction::diagnose(Integral::DEBUG level_a) {

  //---------------------------------------------------------------------------
  //
  // 0. preliminaries
  //
  //---------------------------------------------------------------------------

  // output the class name
  //
  if (level_a > Integral::NONE) {
    SysString output(L"diagnosing class ");
    output.concat(CLASS_NAME);
    output.concat(L": ");
    Console::put(output);
    Console::increaseIndention();
  }

  //  don't exit
  //
  Error::reset();
  Error::set(Error::NONE);
   
  //--------------------------------------------------------------------------
  //
  // 1. required public methods
  //
  //--------------------------------------------------------------------------

  // set indentation
  //
  if (level_a > Integral::NONE) {
    Console::put(L"testing required public methods...\n");
    Console::increaseIndention();
  }

  // test memory manager methods
  //
  Prediction lp;

  Prediction::setGrowSize((long)500);

  Prediction* lpp = new Prediction();
  
  for (long j = 1; j <= 100; j++) {

    Prediction** lp1 = new Prediction*[j * 100];
    
    // create the objects
    //
    for (long i = 0; i < j * 100; i++) {
      lp1[i] = new Prediction();
    }
    
    // delete objects
    //
    for (long i = (j * 100) - 1; i >= 0; i--) {
      delete lp1[i];
    }
      
    delete [] lp1;
  } 

  delete lpp;
  
  // test i/o methods
  //
  Prediction lp0;
  Prediction lp1;
  Prediction lp2;
  lp0.set(COVARIANCE, CHOLESKY, 20, -30);
  
  // we need binary and text sof files
  //
  String tmp_filename0;
  Integral::makeTemp(tmp_filename0);
  String tmp_filename1;
  Integral::makeTemp(tmp_filename1);

  // open files in write mode
  //
  Sof tmp_file0;
  tmp_file0.open(tmp_filename0, File::WRITE_ONLY, File::TEXT);
  Sof tmp_file1;
  tmp_file1.open(tmp_filename1, File::WRITE_ONLY, File::BINARY);

  lp0.write(tmp_file0, (long)0);
  lp0.write(tmp_file1, (long)0);

  // close the files
  //
  tmp_file0.close();
  tmp_file1.close();

  // open the files in read mode
  //
  tmp_file0.open(tmp_filename0);
  tmp_file1.open(tmp_filename1);

  // read the value back
  //
  if (!lp1.read(tmp_file0, (long)0) ||
      (lp1.getOrder() != lp0.getOrder()) ||
      (lp1.getDynRange() != lp0.getDynRange()) ||
      (lp1.getAlgorithm() != lp0.getAlgorithm())) {
    return Error::handle(name(), L"diagnose", Error::TEST, __FILE__, __LINE__);
  }

  if (!lp2.read(tmp_file1, (long)0) ||
      (lp2.getOrder() != lp0.getOrder()) ||
      (lp2.getDynRange() != lp0.getDynRange()) ||
      (lp2.getAlgorithm() != lp0.getAlgorithm())) {
    return Error::handle(name(), L"diagnose", Error::TEST, __FILE__, __LINE__);
  }

  // close and  delete the temporary files
  //
  tmp_file0.close();
  tmp_file1.close();
  File::remove(tmp_filename0);
  File::remove(tmp_filename1);
  
  // test the conditional i/o methods
  //
  Prediction lp_0;
  lp_0.setOrder(20);

  Sof tmp_file2;
  String tmp_filename2;
  Integral::makeTemp(tmp_filename2);

  tmp_file2.open(tmp_filename2, File::WRITE_ONLY);
  lp_0.write(tmp_file2, (long)5);

  // close the file
  //
  tmp_file2.close();

  // open the file in read mode
  //
  tmp_file2.open(tmp_filename2);
  lp_0.read(tmp_file2, (long)5); 

  if (lp_0.getOrder() != (long)20) {
    lp_0.debug(L"lp_0");
    return Error::handle(name(), L"readData", Error::TEST, __FILE__, __LINE__);
  }
  
  lp_0.read(tmp_file2, (long)1);
  
  // close and  delete the temporary files
  //
  tmp_file2.close();
  
  File::remove(tmp_filename2);

  if (level_a > Integral::BRIEF) {
    Console::close();
    File::remove(tmp_filename0);
    Console::put(L"closed and removed temp console");
  }

  // reset indentation
  //
  if (level_a > Integral::NONE) {
    Console::decreaseIndention();
  }
  
  //---------------------------------------------------------------------------
  //
  // 3. class-specific public methods:
  //      set and get methods     
  //
  //---------------------------------------------------------------------------
  
  // set indentation
  //
  if (level_a > Integral::NONE) {  
    Console::put(L"testing class-specific public methods: get and set methods...\n");
    Console::increaseIndention();
  }

  // set and get the order
  //
  Prediction lpc_1;
  lpc_1.setOrder((long)4);
  if (lpc_1.getOrder() != 4) {
    return Error::handle(name(), L"set/get order", Error::TEST,
                         __FILE__, __LINE__);
  }

  // set and get the algorithm
  //
  lpc_1.setAlgorithm(AUTOCORRELATION); 
  if (lpc_1.getAlgorithm() != AUTOCORRELATION) {
    return Error::handle(name(), L"set/get algorithm", Error::TEST,
                         __FILE__, __LINE__);
  }
  
  // set and get the dynamic range
  //
  lpc_1.setDynRange((double)4.5);
  if (lpc_1.getDynRange() != 4.5) {
    return Error::handle(name(), L"set/get DynRange", Error::TEST,
                         __FILE__, __LINE__);
  }
  
  // set and get the set
  //
  long ord;
  ALGORITHM alg;
  IMPLEMENTATION imp;
  float dyn;
  lpc_1.set(AUTOCORRELATION, DURBIN, 4, 4.5);
  lpc_1.get(alg, imp, ord, dyn);
  if ((ord != 4) || (alg != AUTOCORRELATION) || (dyn != 4.5)) {
    return Error::handle(name(), L"set/get set method", Error::TEST,
                         __FILE__, __LINE__);
  }
  
  // reset indentation
  //
  if (level_a > Integral::NONE) {
    Console::decreaseIndention();
  }
  
  //---------------------------------------------------------------------------
  //
  // 4. class-specific public methods:
  //      computational methods     
  //
  //---------------------------------------------------------------------------

  // set indentation
  //
  if (level_a > Integral::NONE) {
    Console::put(L"testing class-specific public methods: computational methods...\n");
    Console::increaseIndention();
  }
  
  // setup vairables for test
  //
  Prediction lpc;
  VectorFloat input;
  VectorFloat pred_coef;
  VectorFloat result_01(L"1, 0, 0, 0, 0");  

  // test LATTICE algorithm
  //
  {
    // use the following data as input:
    // 
    //  x(n) = 0 when n = 0, 1, 2, 3; 
    //  x(n) = 1*pow(0.99, n-4) - pow(0.99, 2(n-4)),  when 4 <= n < 20;
    //  x(n) = 0 when n = 20, 21, 22, 23 
    //
    input.setLength(24);

    float z = 1;
    for (long i = 4; i < 20; i++) {
      input(i) = 2 * z - z * z;
      z = 0.99 * z;
    }    

    // expected prediction coefficients for the autocorrelation,
    // covariance and burg analysis
    //
    VectorFloat res_pred_coef_autoc;
    VectorFloat res_pred_coef_burg;
    res_pred_coef_autoc.assign(L"1.000000, -0.9666891, 0.00009353, 0.00009286241, 0.03525937");
    res_pred_coef_burg.assign(L"1.0000000, -0.9666889, 0.00009363125, 0.00009251215, 0.03525940"); 
    
    // case: algorithm = LATTICE, implementation = BURG,
    //       input = non-zero SIGNAL vector
    //
    lpc.set(LATTICE, BURG, 4, -200);