File content as of revision 0:c5b026c2d07e:

//-----------------------------------------------------
// Class for linear prediction
// Copyright (c) 2014 MIKAMI, Naoki,  2014/12/30
//-----------------------------------------------------

#include "LinearPrediction.hpp"

namespace Mikami
{
    LinearPred::LinearPred(int nData, int order)
        : N_DATA_(nData), ORDER_(order)
    {
        r_ = new float[order+1];    // for auto-correlation
        k_ = new float[order];      // for PARCOR coefficients
        am_ = new float[order];     // working area
    }

    LinearPred::~LinearPred()
    {
        delete[] r_;
        delete[] k_;
        delete[] am_;
    }

    // Calculate linear-predictive coefficients
    bool LinearPred::Execute(const float x[], float a[],
                             float &em)
    {
        AutoCorr(x);
        return Durbin(a, em);
    }

    // Calculate auto-correlation
    void LinearPred::AutoCorr(const float x[])
    {
        for (int j=0; j<=ORDER_; j++)
        {
            r_[j] = 0.0;
            for (int n=0; n<N_DATA_-j; n++)
                r_[j] = r_[j] + x[n]*x[n+j];
        }
    }

    // Levinson-Durbin algorithm
    bool LinearPred::Durbin(float a[], float &em)
    {
        // Initialization
        em = r_[0];

        // Repeat
        for (int m=0; m<ORDER_; m++)
        {
            float w = r_[m+1];
            for (int j=0; j<=m-1; j++)
                w = w - r_[m-j]*a[j];

            k_[m] = w/em;
            em = em*(1 - k_[m]*k_[m]);

            if (em < 0) break;      // Error for negative squared sum of residual

            a[m] = k_[m];
            for (int j=0; j<=m-1; j++)
                am_[j] = a[j];
            for (int j=0; j<=m-1; j++)
                a[j] = am_[j] - k_[m]*am_[m-j-1];
        }

        if (em < 0) return false;
        else        return true;
    }
}