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/******************************************************
*
* Adaptive Systems for PD
*
* copyleft (c) Gerda Strobl, Georg Holzmann
* 2005
*
* for complaints, suggestions: grh@mur.at
*
******************************************************
*
* license: GNU General Public License v.2
*
******************************************************/
#include "adaptive.h"
/* ------------------------ nlms~ ------------------------- */
static t_class *nlms_tilde_class;
typedef struct _nlms
{
t_object x_obj;
t_float f;
t_sample *buf;
t_sample *tmp;
t_int bufsize;
int adapt; // enable/disable adaptation
t_int N; //number of coefficients of the adaptive system
t_float *c; // coefficients of the system
t_float mu; // step-size parameter (learning rate)
t_float alpha; // small constant to avoid division by zero
t_canvas *x_canvas;
} t_nlms_tilde;
static void nlms_tilde_a(t_nlms_tilde *x, t_floatarg f)
{
x->adapt = (f==0) ? 0 : 1;
}
static void nlms_tilde_geta(t_nlms_tilde *x)
{
if(x->adapt==0)
post("nlms~: adaptation is currently OFF");
else
post("nlms~: adaptation is currently ON");
}
static void nlms_tilde_mu(t_nlms_tilde *x, t_floatarg f)
{
x->mu = f;
}
static void nlms_tilde_getmu(t_nlms_tilde *x)
{
post("mu (step-size parameter): %f", x->mu);
}
static void nlms_tilde_alpha(t_nlms_tilde *x, t_floatarg f)
{
x->alpha = f;
}
static void nlms_tilde_getalpha(t_nlms_tilde *x)
{
post("alpha: %f", x->alpha);
}
static void nlms_tilde_getN(t_nlms_tilde *x)
{
post("N (number of coefficients): %d", x->N);
}
static void nlms_tilde_clear(t_nlms_tilde *x)
{
int i;
// clear coefficients
for(i=0; i<x->N; i++)
x->c[i] = 0;
// clear temp buffer
for(i=0; i<x->N-1; i++)
x->buf[i] = 0;
}
static void nlms_tilde_init(t_nlms_tilde *x)
{
int i;
// set the first coefficient to 1, all others to 0
// so this is a delay free transmission
x->c[0] = 1;
for(i=1; i<x->N; i++)
x->c[i] = 0;
// clear temp buffers
for(i=0; i<x->N-1; i++)
x->buf[i] = 0;
}
static void nlms_tilde_print(t_nlms_tilde *x)
{
int i;
// print coefficients
post("\nNr. of coefficients: %d",x->N);
post("coefficients:");
for(i=0; i<x->N; i++)
post("\t%d: %f",i,x->c[i]);
}
static void nlms_tilde_write(t_nlms_tilde *x, t_symbol *s)
{
// make correct path
char filnam[MAXPDSTRING];
char filename[MAXPDSTRING];
canvas_makefilename(x->x_canvas, s->s_name, filnam, MAXPDSTRING);
sys_bashfilename(filnam, filename);
// save to file
adaptation_write(filename, x->N, x->mu, x->c);
}
static void nlms_tilde_read(t_nlms_tilde *x, t_symbol *s)
{
// make correct path
char filnam[MAXPDSTRING];
char filename[MAXPDSTRING];
canvas_makefilename(x->x_canvas, s->s_name, filnam, MAXPDSTRING);
sys_bashfilename(filnam, filename);
// read file
adaptation_read(filename, &x->N, &x->mu, x->c, x->buf);
}
static t_int *nlms_tilde_perform(t_int *w)
{
t_nlms_tilde *x = (t_nlms_tilde *)(w[1]);
t_sample *x_ = (t_sample *)(w[2]);
t_sample *d_ = (t_sample *)(w[3]);
t_sample *y_ = (t_sample *)(w[4]);
int n = (int)(w[5]);
int i, j, tmp;
t_sample e=0, x_2;
for(i=0; i<n; i++)
{
// calc output (filter)
x->tmp[i]=0;
// y_[i] += x->c[j] * x_[i-j];
// so lets split in two halfs, so that
// negative indezes get samples from the
// last audioblock (x->buf) ...
tmp = (i+1 - x->N)*(-1);
tmp = tmp<0 ? 0 : tmp;
for(j=0; j<x->N-tmp; j++)
x->tmp[i] += x->c[j] * x_[i-j];
for(j=x->N-tmp; j<x->N; j++)
x->tmp[i] += x->c[j] * x->buf[(i-j)*(-1)-1];
if(x->adapt)
{
x_2=0;
// error computation
e =d_[i] - x->tmp[i];
// Normalized LMS Adaptmsation Algorithm
// (split in the same way as above)
//
// c[n] = c[n-1] + mu/(alpha + x'[n]*x[n])*e[n]*x[n]
// calc x'[n]*x[n]
// TODO: Performance Optimization: save results from the past
// so that this for loop should be obsolet ...
for(j=0; j<x->N-tmp; j++)
x_2 += x_[i-j] * x_[i-j];
for(j=x->N-tmp; j<x->N; j++)
x_2 += x->buf[(i-j)*(-1)-1] * x->buf[(i-j)*(-1)-1];
for(j=0; j<x->N-tmp; j++)
x->c[j] = x->c[j] + x->mu/(x->alpha+x_2) * x_[i-j] * e;
for(j=x->N-tmp; j<x->N; j++)
x->c[j] = x->c[j] + x->mu/(x->alpha+x_2) * x->buf[(i-j)*(-1)-1] * e;
}
//post("%d: in %f, d: %f, out: %f, error: %f, c1:%f, c2:%f", i, x_[i], d_[i], x->tmp[i], e, x->c[0], x->c[1]);
}
// store last samples for next audiobuffer
for(i=0; i<x->N-1; i++)
x->buf[i] = x_[n-1-i];
// now write tmp to outlet
while(n--)
y_[n] = x->tmp[n];
return (w+6);
}
static void nlms_tilde_dsp(t_nlms_tilde *x, t_signal **sp)
{
// allocate new temp buffer if buffersize changes
if(x->bufsize != sp[0]->s_n)
{
if(sp[0]->s_n < x->N)
post("nlms~ WARNING: buffersize must be bigger than N, you will get wrong results !!!");
if(x->tmp) freebytes(x->tmp, sizeof(t_sample) * x->bufsize);
x->tmp = (t_sample *)getbytes(sizeof(t_sample) * sp[0]->s_n);
x->bufsize = sp[0]->s_n;
}
dsp_add(nlms_tilde_perform, 5, x, sp[0]->s_vec, sp[1]->s_vec,
sp[2]->s_vec, sp[0]->s_n);
}
static void nlms_tilde_helper(void)
{
post("\nnlms~: Adaptive transversal filter using normalized LMS");
post("INPUT:");
post("\tinlet1: input signal x[n]");
post("\tinlet2: desired output signal d[n]");
post("\tinit_arg1: number of coefficients of the adaptive system");
post("\tinit_arg2, mu: step-size parameter (learning rate)");
post("OUTPUT:");
post("\toutlet1: output signal\n");
}
static void *nlms_tilde_new(t_symbol *s, int argc, t_atom *argv)
{
t_nlms_tilde *x = (t_nlms_tilde *)pd_new(nlms_tilde_class);
int i;
// default values:
x->N = 8;
x->mu = 0.05;
x->alpha = 0.0001;
x->adapt = 0;
x->tmp = NULL;
x->bufsize = 0;
switch(argc)
{
case 2:
x->mu = atom_getfloat(argv+1);
case 1:
x->N = atom_getint(argv);
x->N = (x->N<=0) ? 1 : x->N;
}
// allocate mem and init coefficients
x->c = (t_float *)getbytes(sizeof(t_float) * x->N);
for(i=0; i<x->N; i++)
x->c[i] = 0;
// allocate mem for temp buffer
x->buf = (t_sample *)getbytes(sizeof(t_sample) * x->N-1);
for(i=0; i<x->N-1; i++)
x->buf[i] = 0;
inlet_new(&x->x_obj, &x->x_obj.ob_pd, &s_signal, &s_signal);
outlet_new(&x->x_obj, &s_signal);
x->x_canvas = canvas_getcurrent();
return (x);
}
static void nlms_tilde_free(t_nlms_tilde *x)
{
if(x->c) freebytes(x->c, sizeof(t_float) * x->N);
if(x->buf) freebytes(x->buf, sizeof(t_sample) * x->N-1);
if(x->tmp) freebytes(x->tmp, sizeof(t_sample) * x->bufsize);
}
void nlms_tilde_setup(void)
{
nlms_tilde_class = class_new(gensym("nlms~"), (t_newmethod)nlms_tilde_new,
(t_method)nlms_tilde_free, sizeof(t_nlms_tilde),
CLASS_DEFAULT, A_GIMME, 0);
class_addmethod(nlms_tilde_class, (t_method)nlms_tilde_a,
gensym("adaptation"), A_DEFFLOAT, 0);
class_addmethod(nlms_tilde_class, (t_method)nlms_tilde_geta,
gensym("getadaptation"), 0);
class_addmethod(nlms_tilde_class, (t_method)nlms_tilde_mu,
gensym("mu"), A_DEFFLOAT, 0);
class_addmethod(nlms_tilde_class, (t_method)nlms_tilde_getmu,
gensym("getmu"), 0);
class_addmethod(nlms_tilde_class, (t_method)nlms_tilde_alpha,
gensym("alpha"), A_DEFFLOAT, 0);
class_addmethod(nlms_tilde_class, (t_method)nlms_tilde_getalpha,
gensym("getalpha"), 0);
class_addmethod(nlms_tilde_class, (t_method)nlms_tilde_getN,
gensym("getN"), 0);
class_addmethod(nlms_tilde_class, (t_method)nlms_tilde_init,
gensym("init_unity"), 0);
class_addmethod(nlms_tilde_class, (t_method)nlms_tilde_clear,
gensym("clear"), 0);
class_addmethod(nlms_tilde_class, (t_method)nlms_tilde_print,
gensym("print"), 0);
class_addmethod(nlms_tilde_class, (t_method)nlms_tilde_write,
gensym("write"), A_DEFSYMBOL, 0);
class_addmethod(nlms_tilde_class, (t_method)nlms_tilde_read,
gensym("read"), A_DEFSYMBOL, 0);
class_addmethod(nlms_tilde_class, (t_method)nlms_tilde_dsp, gensym("dsp"), 0);
CLASS_MAINSIGNALIN(nlms_tilde_class, t_nlms_tilde, f);
class_addmethod(nlms_tilde_class, (t_method)nlms_tilde_helper, gensym("help"), 0);
}
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