diff options
Diffstat (limited to 'gr-vocoder/lib/codec2/lpc.c')
| -rw-r--r-- | gr-vocoder/lib/codec2/lpc.c | 94 |
1 files changed, 62 insertions, 32 deletions
diff --git a/gr-vocoder/lib/codec2/lpc.c b/gr-vocoder/lib/codec2/lpc.c index 1784f75ca1..9a730eb4ad 100644 --- a/gr-vocoder/lib/codec2/lpc.c +++ b/gr-vocoder/lib/codec2/lpc.c @@ -2,14 +2,14 @@ FILE........: lpc.c AUTHOR......: David Rowe - DATE CREATED: 30/9/90 + DATE CREATED: 30 Sep 1990 (!) Linear Prediction functions written in C. \*---------------------------------------------------------------------------*/ /* - Copyright (C) 2009 David Rowe + Copyright (C) 2009-2012 David Rowe All rights reserved. @@ -28,6 +28,9 @@ #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" @@ -35,6 +38,60 @@ /*---------------------------------------------------------------------------*\ + 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 hensea 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 polse 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. @@ -50,7 +107,7 @@ void hanning_window( int i; /* loop variable */ for(i=0; i<Nsam; i++) - Wn[i] = Sn[i]*(0.5 - 0.5*cos(2*PI*(float)i/(Nsam-1))); + Wn[i] = Sn[i]*(0.5 - 0.5*cosf(2*PI*(float)i/(Nsam-1))); } /*---------------------------------------------------------------------------*\ @@ -80,33 +137,6 @@ void autocorrelate( /*---------------------------------------------------------------------------*\ - autocorrelate_freq() - - Finds the first P autocorrelation values from an array of frequency domain - power samples. - -\*---------------------------------------------------------------------------*/ - -void autocorrelate_freq( - float Pw[], /* Nsam frequency domain power spectrum samples */ - float w[], /* frequency of each sample in Pw[] */ - float R[], /* array of order+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++) { - R[j] = 0.0; - for(i=0; i<Nsam; i++) - R[j] += Pw[i]*cos(j*w[i]); - } - R[j] /= Nsam; -} - -/*---------------------------------------------------------------------------*\ - levinson_durbin() Given P+1 autocorrelation coefficients, finds P Linear Prediction Coeff. @@ -139,7 +169,7 @@ void levinson_durbin( for(j=1; j<=i-1; j++) sum += a[i-1][j]*R[i-j]; k[i] = -1.0*(R[i] + sum)/E[i-1]; /* Equation 38b, Makhoul */ - if (fabs(k[i]) > 1.0) + if (fabsf(k[i]) > 1.0) k[i] = 0.0; a[i][i] = k[i]; @@ -274,6 +304,6 @@ void weight( int i; for(i=1; i<=order; i++) - akw[i] = ak[i]*pow(gamma,(float)i); + akw[i] = ak[i]*powf(gamma,(float)i); } |