shallow zip-file copy from codec2 e9d726bf20

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diff --git a/src/lpc.c b/src/lpc.c
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+/*---------------------------------------------------------------------------*\
+
+  FILE........: lpc.c
+  AUTHOR......: David Rowe
+  DATE CREATED: 30 Sep 1990 (!)
+
+  Linear Prediction functions written in C.
+
+\*---------------------------------------------------------------------------*/
+
+/*
+  Copyright (C) 2009-2012 David Rowe
+
+  All rights reserved.
+
+  This program is free software; you can redistribute it and/or modify
+  it under the terms of the GNU Lesser General Public License version 2.1, as
+  published by the Free Software Foundation.  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 Lesser General Public License
+  along with this program; if not, see <http://www.gnu.org/licenses/>.
+*/
+
+#define LPC_MAX_N 512		/* maximum no. of samples in frame */
+#define PI 3.141592654		/* mathematical constant */
+
+#define ALPHA 1.0
+#define BETA  0.94
+
+#include <assert.h>
+#include <math.h>
+#include "defines.h"
+#include "lpc.h"
+
+/*---------------------------------------------------------------------------*\
+
+  pre_emp()
+
+  Pre-emphasise (high pass filter with zero close to 0 Hz) a frame of
+  speech samples.  Helps reduce dynamic range of LPC spectrum, giving
+  greater weight and hense a better match to low energy formants.
+
+  Should be balanced by de-emphasis of the output speech.
+
+\*---------------------------------------------------------------------------*/
+
+void pre_emp(
+  float  Sn_pre[], /* output frame of speech samples                     */
+  float  Sn[],	   /* input frame of speech samples                      */
+  float *mem,      /* Sn[-1]single sample memory                         */
+  int   Nsam	   /* number of speech samples to use                    */
+)
+{
+    int   i;
+
+    for(i=0; i<Nsam; i++) {
+	Sn_pre[i] = Sn[i] - ALPHA * mem[0];
+	mem[0] = Sn[i];
+    }
+
+}
+
+
+/*---------------------------------------------------------------------------*\
+
+  de_emp()
+
+  De-emphasis filter (low pass filter with a pole close to 0 Hz).
+
+\*---------------------------------------------------------------------------*/
+
+void de_emp(
+  float  Sn_de[],  /* output frame of speech samples                     */
+  float  Sn[],	   /* input frame of speech samples                      */
+  float *mem,      /* Sn[-1]single sample memory                         */
+  int    Nsam	   /* number of speech samples to use                    */
+)
+{
+    int   i;
+
+    for(i=0; i<Nsam; i++) {
+	Sn_de[i] = Sn[i] + BETA * mem[0];
+	mem[0] = Sn_de[i];
+    }
+
+}
+
+
+/*---------------------------------------------------------------------------*\
+
+  hanning_window()
+
+  Hanning windows a frame of speech samples.
+
+\*---------------------------------------------------------------------------*/
+
+void hanning_window(
+  float Sn[],	/* input frame of speech samples */
+  float Wn[],	/* output frame of windowed samples */
+  int Nsam	/* number of samples */
+)
+{
+  int i;	/* loop variable */
+
+  for(i=0; i<Nsam; i++)
+    Wn[i] = Sn[i]*(0.5 - 0.5*cosf(2*PI*(float)i/(Nsam-1)));
+}
+
+/*---------------------------------------------------------------------------*\
+
+  autocorrelate()
+
+  Finds the first P autocorrelation values of an array of windowed speech
+  samples Sn[].
+
+\*---------------------------------------------------------------------------*/
+
+void autocorrelate(
+  float Sn[],	/* frame of Nsam windowed speech samples */
+  float Rn[],	/* array of P+1 autocorrelation coefficients */
+  int Nsam,	/* number of windowed samples to use */
+  int order	/* order of LPC analysis */
+)
+{
+  int i,j;	/* loop variables */
+
+  for(j=0; j<order+1; j++) {
+    Rn[j] = 0.0;
+    for(i=0; i<Nsam-j; i++)
+      Rn[j] += Sn[i]*Sn[i+j];
+  }
+}
+
+/*---------------------------------------------------------------------------*\
+
+  levinson_durbin()
+
+  Given P+1 autocorrelation coefficients, finds P Linear Prediction Coeff.
+  (LPCs) where P is the order of the LPC all-pole model. The Levinson-Durbin
+  algorithm is used, and is described in:
+
+    J. Makhoul
+    "Linear prediction, a tutorial review"
+    Proceedings of the IEEE
+    Vol-63, No. 4, April 1975
+
+\*---------------------------------------------------------------------------*/
+
+void levinson_durbin(
+  float R[],		/* order+1 autocorrelation coeff */
+  float lpcs[],		/* order+1 LPC's */
+  int order		/* order of the LPC analysis */
+)
+{
+  float a[order+1][order+1];
+  float sum, e, k;
+  int i,j;				/* loop variables */
+
+  e = R[0];				/* Equation 38a, Makhoul */
+
+  for(i=1; i<=order; i++) {
+    sum = 0.0;
+    for(j=1; j<=i-1; j++)
+      sum += a[i-1][j]*R[i-j];
+    k = -1.0*(R[i] + sum)/e;		/* Equation 38b, Makhoul */
+    if (fabsf(k) > 1.0)
+      k = 0.0;
+
+    a[i][i] = k;
+
+    for(j=1; j<=i-1; j++)
+      a[i][j] = a[i-1][j] + k*a[i-1][i-j];	/* Equation 38c, Makhoul */
+
+    e *= (1-k*k);				/* Equation 38d, Makhoul */
+  }
+
+  for(i=1; i<=order; i++)
+    lpcs[i] = a[order][i];
+  lpcs[0] = 1.0;
+}
+
+/*---------------------------------------------------------------------------*\
+
+  inverse_filter()
+
+  Inverse Filter, A(z).  Produces an array of residual samples from an array
+  of input samples and linear prediction coefficients.
+
+  The filter memory is stored in the first order samples of the input array.
+
+\*---------------------------------------------------------------------------*/
+
+void inverse_filter(
+  float Sn[],	/* Nsam input samples */
+  float a[],	/* LPCs for this frame of samples */
+  int Nsam,	/* number of samples */
+  float res[],	/* Nsam residual samples */
+  int order	/* order of LPC */
+)
+{
+  int i,j;	/* loop variables */
+
+  for(i=0; i<Nsam; i++) {
+    res[i] = 0.0;
+    for(j=0; j<=order; j++)
+      res[i] += Sn[i-j]*a[j];
+  }
+}
+
+/*---------------------------------------------------------------------------*\
+
+ synthesis_filter()
+
+ C version of the Speech Synthesis Filter, 1/A(z).  Given an array of
+ residual or excitation samples, and the the LP filter coefficients, this
+ function will produce an array of speech samples.  This filter structure is
+ IIR.
+
+ The synthesis filter has memory as well, this is treated in the same way
+ as the memory for the inverse filter (see inverse_filter() notes above).
+ The difference is that the memory for the synthesis filter is stored in
+ the output array, whereas the memory of the inverse filter is stored in the
+ input array.
+
+ Note: the calling function must update the filter memory.
+
+\*---------------------------------------------------------------------------*/
+
+void synthesis_filter(
+  float res[],	/* Nsam input residual (excitation) samples */
+  float a[],	/* LPCs for this frame of speech samples */
+  int Nsam,	/* number of speech samples */
+  int order,	/* LPC order */
+  float Sn_[]	/* Nsam output synthesised speech samples */
+)
+{
+  int i,j;	/* loop variables */
+
+  /* Filter Nsam samples */
+
+  for(i=0; i<Nsam; i++) {
+    Sn_[i] = res[i]*a[0];
+    for(j=1; j<=order; j++)
+      Sn_[i] -= Sn_[i-j]*a[j];
+  }
+}
+
+/*---------------------------------------------------------------------------*\
+
+  find_aks()
+
+  This function takes a frame of samples, and determines the linear
+  prediction coefficients for that frame of samples.
+
+\*---------------------------------------------------------------------------*/
+
+void find_aks(
+  float Sn[],	/* Nsam samples with order sample memory */
+  float a[],	/* order+1 LPCs with first coeff 1.0 */
+  int Nsam,	/* number of input speech samples */
+  int order,	/* order of the LPC analysis */
+  float *E	/* residual energy */
+)
+{
+  float Wn[LPC_MAX_N];	/* windowed frame of Nsam speech samples */
+  float R[order+1];	/* order+1 autocorrelation values of Sn[] */
+  int i;
+
+  assert(Nsam < LPC_MAX_N);
+
+  hanning_window(Sn,Wn,Nsam);
+  autocorrelate(Wn,R,Nsam,order);
+  levinson_durbin(R,a,order);
+
+  *E = 0.0;
+  for(i=0; i<=order; i++)
+    *E += a[i]*R[i];
+  if (*E < 0.0)
+    *E = 1E-12;
+}
+
+/*---------------------------------------------------------------------------*\
+
+  weight()
+
+  Weights a vector of LPCs.
+
+\*---------------------------------------------------------------------------*/
+
+void weight(
+  float ak[],	/* vector of order+1 LPCs */
+  float gamma,	/* weighting factor */
+  int order,	/* num LPCs (excluding leading 1.0) */
+  float akw[]	/* weighted vector of order+1 LPCs */
+)
+{
+  int i;
+
+  for(i=1; i<=order; i++)
+    akw[i] = ak[i]*powf(gamma,(float)i);
+}
+