mirror of https://github.com/ShaYmez/MMDVM_CM.git
312 lines
8.2 KiB
C++
312 lines
8.2 KiB
C++
/*---------------------------------------------------------------------------*\
|
|
|
|
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 Clpc::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 Clpc::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 Clpc::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 Clpc::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 Clpc::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 Clpc::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, wheras the memory of the inverse filter is stored in the
|
|
input array.
|
|
|
|
Note: the calling function must update the filter memory.
|
|
|
|
\*---------------------------------------------------------------------------*/
|
|
|
|
void Clpc::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 Clpc::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 Clpc::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);
|
|
}
|