[PD-cvs] externals/iem/iem_adaptfilt/src n_CLNLMS~.c, NONE, 1.1 n_CNLMS~.c, NONE, 1.1 NLMS~.c, NONE, 1.1 NLMSCC~.c, NONE, 1.1 iem_adaptfilt.c, 1.1, 1.2 makefile_lin, 1.1, 1.2 makefile.txt, 1.1, 1.2 makefile_win, 1.1, 1.2 makefile_win.txt, 1.1, 1.2
Georg Holzmann
grholzi at users.sourceforge.net
Thu Jan 11 18:40:12 CET 2007
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Update of /cvsroot/pure-data/externals/iem/iem_adaptfilt/src
In directory sc8-pr-cvs1.sourceforge.net:/tmp/cvs-serv7807
Modified Files:
iem_adaptfilt.c makefile_lin makefile.txt makefile_win
makefile_win.txt
Added Files:
n_CLNLMS~.c n_CNLMS~.c NLMS~.c NLMSCC~.c
Log Message:
changed files to new naming convention
Index: makefile_lin
===================================================================
RCS file: /cvsroot/pure-data/externals/iem/iem_adaptfilt/src/makefile_lin,v
retrieving revision 1.1
retrieving revision 1.2
diff -C2 -d -r1.1 -r1.2
*** makefile_lin 2 Aug 2006 14:02:28 -0000 1.1
--- makefile_lin 11 Jan 2007 17:40:10 -0000 1.2
***************
*** 3,7 ****
.SUFFIXES: .pd_linux
! INCLUDE = -I. -I/usr/local/src/pd-0.37-1/src
LDFLAGS = -export-dynamic -shared
--- 3,7 ----
.SUFFIXES: .pd_linux
! INCLUDE = -I. -I/usr/local/src/pd/src
LDFLAGS = -export-dynamic -shared
***************
*** 10,14 ****
#select either the DBG and OPT compiler flags below:
! CFLAGS = -DPD -DUNIX -W -Werror -Wno-unused \
-Wno-parentheses -Wno-switch -O6 -funroll-loops -fomit-frame-pointer \
-DDL_OPEN
--- 10,14 ----
#select either the DBG and OPT compiler flags below:
! CFLAGS = -DPD -DUNIX -W -Wno-unused \
-Wno-parentheses -Wno-switch -O6 -funroll-loops -fomit-frame-pointer \
-DDL_OPEN
***************
*** 18,25 ****
# the sources
! SRC = sigNLMS.c \
! sigNLMSCC.c \
! sign_CNLMS.c \
! sign_CLNLMS.c \
iem_adaptfilt.c
--- 18,25 ----
# the sources
! SRC = NLMS~.c \
! NLMSCC~.c \
! n_CNLMS~.c \
! n_CLNLMS~.c \
iem_adaptfilt.c
Index: makefile_win.txt
===================================================================
RCS file: /cvsroot/pure-data/externals/iem/iem_adaptfilt/src/makefile_win.txt,v
retrieving revision 1.1
retrieving revision 1.2
diff -C2 -d -r1.1 -r1.2
*** makefile_win.txt 2 Aug 2006 14:02:28 -0000 1.1
--- makefile_win.txt 11 Jan 2007 17:40:10 -0000 1.2
***************
*** 22,29 ****
! SRC = sigNLMS.c \
! sigNLMSCC.c \
! sign_CNLMS.c \
! sign_CLNLMS.c \
iem_adaptfilt.c
--- 22,29 ----
! SRC = NLMS~.c \
! NLMSCC~.c \
! n_CNLMS~.c \
! n_CLNLMS~.c \
iem_adaptfilt.c
--- NEW FILE: n_CNLMS~.c ---
/* For information on usage and redistribution, and for a DISCLAIMER OF ALL
* WARRANTIES, see the file, "LICENSE.txt," in this distribution.
n_CNLMS multichannel-constrained (non leaky) normalized LMS algorithm
lib iem_adaptfilt written by Markus Noisternig & Thomas Musil
noisternig_AT_iem.at; musil_AT_iem.at
(c) Institute of Electronic Music and Acoustics, Graz Austria 2005 */
#ifdef NT
#pragma warning( disable : 4244 )
#pragma warning( disable : 4305 )
#endif
#include "m_pd.h"
#include "iemlib.h"
#include <math.h>
#include <stdio.h>
#include <string.h>
/* ----------------------- n_CNLMS~ ------------------------------ */
/* -- multi-channel Constraint Normalized Least Mean Square (linear adaptive FIR-filter) -- */
/* -- first input: reference signal -- */
/* -- second input: desired signal -- */
/* -- -- */
/* for further information on adaptive filter design we refer to */
/* [1] Haykin, "Adaptive Filter Theory", 4th ed, Prentice Hall */
/* [2] Benesty, "Adaptive Signal Processing", Springer */
typedef struct n_CNLMS_tilde_kern
{
t_symbol *x_w_array_sym_name;
t_float *x_w_array_mem_beg;
t_float *x_in_ptr_beg;// memory: sig-in vector
t_float *x_out_ptr_beg;// memory: sig-out vector
t_float *x_in_hist;// start point double buffer for sig-in history
} t_n_CNLMS_tilde_kern;
typedef struct n_CNLMS_tilde
{
t_object x_obj;
t_n_CNLMS_tilde_kern *x_my_kern;
t_float *x_des_in_ptr_beg;// memory: desired-in vector
t_float *x_err_out_ptr_beg;// memory: error-out vector
t_int x_n_io;// number of in-channels and filtered out-channels
t_int x_rw_index;// read-write-index
t_int x_n_order;// filter order
t_int x_update;// rounded by 2^n, yields downsampling of update rate
t_float x_beta;// learn rate [0 .. 2]
t_float x_gamma;// normalization
t_float x_kappa;// constraint: threshold of energy (clipping)
t_outlet *x_out_compressing_bang;
t_clock *x_clock;
t_float x_msi;
} t_n_CNLMS_tilde;
t_class *n_CNLMS_tilde_class;
static void n_CNLMS_tilde_tick(t_n_CNLMS_tilde *x)
{
outlet_bang(x->x_out_compressing_bang);
}
static t_float *n_CNLMS_tilde_check_array(t_symbol *array_sym_name, t_int length)
{
t_int n_points;
t_garray *a;
t_float *vec;
if(!(a = (t_garray *)pd_findbyclass(array_sym_name, garray_class)))
{
error("%s: no such array for n_CNLMS~", array_sym_name->s_name);
return((t_float *)0);
}
else if(!garray_getfloatarray(a, &n_points, &vec))
{
error("%s: bad template for n_CNLMS~", array_sym_name->s_name);
return((t_float *)0);
}
else if(n_points < length)
{
error("%s: bad array-size for n_CNLMS~: %d", array_sym_name->s_name, n_points);
return((t_float *)0);
}
else
{
return(vec);
}
}
static void n_CNLMS_tilde_beta(t_n_CNLMS_tilde *x, t_floatarg f) // learn rate
{
if(f < 0.0f)
f = 0.0f;
if(f > 2.0f)
f = 2.0f;
x->x_beta = f;
}
static void n_CNLMS_tilde_gamma(t_n_CNLMS_tilde *x, t_floatarg f) // regularization (dither)
{
if(f < 0.0f)
f = 0.0f;
if(f > 1.0f)
f = 1.0f;
x->x_gamma = f;
}
static void n_CNLMS_tilde_kappa(t_n_CNLMS_tilde *x, t_floatarg f) // threshold for w_coeff
{
if(f < 0.0001f)
f = 0.0001f;
if(f > 10000.0f)
f = 10000.0f;
x->x_kappa = f;
}
static void n_CNLMS_tilde_update(t_n_CNLMS_tilde *x, t_floatarg f) // downsampling of learn rate
{
t_int i=1, u = (t_int)f;
if(u < 0)
u = 0;
else
{
while(i <= u) // convert u for 2^N
i *= 2; // round downward
i /= 2;
u = i;
}
x->x_update = u;
}
/* ============== DSP ======================= */
static t_int *n_CNLMS_tilde_perform_zero(t_int *w)
{
t_n_CNLMS_tilde *x = (t_n_CNLMS_tilde *)(w[1]);
t_int n = (t_int)(w[2]);
t_int n_io = x->x_n_io;
t_float *out;
t_int i, j;
out = x->x_err_out_ptr_beg;
for(i=0; i<n; i++)
*out++ = 0.0f;
for(j=0; j<n_io; j++)
{
out = x->x_my_kern[j].x_out_ptr_beg;
for(i=0; i<n; i++)
*out++ = 0.0f;
}
return (w+3);
}
static t_int *n_CNLMS_tilde_perform(t_int *w)
{
t_n_CNLMS_tilde *x = (t_n_CNLMS_tilde *)(w[1]);
t_int n = (t_int)(w[2]);
t_int n_order = x->x_n_order; /* filter-order */
t_int rw_index2, rw_index = x->x_rw_index;
t_int n_io = x->x_n_io;
t_float *in;// first sig in
t_float din;// second sig in
t_float *filt_out;// first sig out
t_float *err_out, err_sum;// second sig out
t_float *read_in_hist;
t_float *w_filt_coeff;
t_float my, my_err, sum;
t_float beta = x->x_beta;
t_float hgamma, gamma = x->x_gamma;
t_float hkappa, kappa = x->x_kappa;
t_int i, j, k, update_counter;
t_int update = x->x_update;
t_int ord8=n_order&0xfffffff8;
t_int ord_residual=n_order&0x7;
t_int compressed = 0;
for(k=0; k<n_io; k++)
{
if(!x->x_my_kern[k].x_w_array_mem_beg)
goto n_CNLMS_tildeperfzero;// this is Musil/Miller style
}
hgamma = gamma * gamma * (float)n_order;
//hkappa = kappa * kappa * (float)n_order;
hkappa = kappa;// kappa regards to energy value, else use line above
for(i=0, update_counter=0; i<n; i++)// history and (block-)convolution
{
rw_index2 = rw_index + n_order;
for(k=0; k<n_io; k++)// times n_io
{
x->x_my_kern[k].x_in_hist[rw_index] = x->x_my_kern[k].x_in_ptr_beg[i]; // save inputs into variabel & history
x->x_my_kern[k].x_in_hist[rw_index+n_order] = x->x_my_kern[k].x_in_ptr_beg[i];
}
din = x->x_des_in_ptr_beg[i];
// begin convolution
err_sum = din;
for(k=0; k<n_io; k++)// times n_io
{
sum = 0.0f;
w_filt_coeff = x->x_my_kern[k].x_w_array_mem_beg; // Musil's special convolution buffer struct
read_in_hist = &x->x_my_kern[k].x_in_hist[rw_index2];
for(j=0; j<ord8; j+=8) // loop unroll 8 taps
{
sum += w_filt_coeff[0] * read_in_hist[0];
sum += w_filt_coeff[1] * read_in_hist[-1];
sum += w_filt_coeff[2] * read_in_hist[-2];
sum += w_filt_coeff[3] * read_in_hist[-3];
sum += w_filt_coeff[4] * read_in_hist[-4];
sum += w_filt_coeff[5] * read_in_hist[-5];
sum += w_filt_coeff[6] * read_in_hist[-6];
sum += w_filt_coeff[7] * read_in_hist[-7];
w_filt_coeff += 8;
read_in_hist -= 8;
}
for(j=0; j<ord_residual; j++) // for filter order < 2^N
sum += w_filt_coeff[j] * read_in_hist[-j];
x->x_my_kern[k].x_out_ptr_beg[i] = sum;
err_sum -= sum;
}
x->x_err_out_ptr_beg[i] = err_sum;
// end convolution
if(update) // downsampling of learn rate
{
update_counter++;
if(update_counter >= update)
{
update_counter = 0;
for(k=0; k<n_io; k++)// times n_io
{
sum = 0.0f;// calculate energy for last n-order samples in filter
read_in_hist = &x->x_my_kern[k].x_in_hist[rw_index2];
for(j=0; j<ord8; j+=8) // unrolling quadrature calc
{
sum += read_in_hist[0] * read_in_hist[0];
sum += read_in_hist[-1] * read_in_hist[-1];
sum += read_in_hist[-2] * read_in_hist[-2];
sum += read_in_hist[-3] * read_in_hist[-3];
sum += read_in_hist[-4] * read_in_hist[-4];
sum += read_in_hist[-5] * read_in_hist[-5];
sum += read_in_hist[-6] * read_in_hist[-6];
sum += read_in_hist[-7] * read_in_hist[-7];
read_in_hist -= 8;
}
for(j=0; j<ord_residual; j++) // residual
sum += read_in_hist[-j] * read_in_hist[-j]; // [-j] only valid for Musil's double buffer structure
sum += hgamma; // convert gamma corresponding to filter order
my = beta / sum;// calculate mue
my_err = my * err_sum;
w_filt_coeff = x->x_my_kern[k].x_w_array_mem_beg;
read_in_hist = &x->x_my_kern[k].x_in_hist[rw_index2];
sum = 0.0f;
for(j=0; j<ord8; j+=8) // unrolling quadrature calc
{
w_filt_coeff[0] += read_in_hist[0] * my_err;
sum += w_filt_coeff[0] * w_filt_coeff[0];
w_filt_coeff[1] += read_in_hist[-1] * my_err;
sum += w_filt_coeff[1] * w_filt_coeff[1];
w_filt_coeff[2] += read_in_hist[-2] * my_err;
sum += w_filt_coeff[2] * w_filt_coeff[2];
w_filt_coeff[3] += read_in_hist[-3] * my_err;
sum += w_filt_coeff[3] * w_filt_coeff[3];
w_filt_coeff[4] += read_in_hist[-4] * my_err;
sum += w_filt_coeff[4] * w_filt_coeff[4];
w_filt_coeff[5] += read_in_hist[-5] * my_err;
sum += w_filt_coeff[5] * w_filt_coeff[5];
w_filt_coeff[6] += read_in_hist[-6] * my_err;
sum += w_filt_coeff[6] * w_filt_coeff[6];
w_filt_coeff[7] += read_in_hist[-7] * my_err;
sum += w_filt_coeff[7] * w_filt_coeff[7];
w_filt_coeff += 8;
read_in_hist -= 8;
}
for(j=0; j<ord_residual; j++) // residual
{
w_filt_coeff[j] += read_in_hist[-j] * my_err;
sum += w_filt_coeff[j] * w_filt_coeff[j];
}
if(sum > hkappa)
{
compressed = 1;
my = sqrt(hkappa/sum);
w_filt_coeff = x->x_my_kern[k].x_w_array_mem_beg;
for(j=0; j<ord8; j+=8) // unrolling quadrature calc
{
w_filt_coeff[0] *= my;
w_filt_coeff[1] *= my;
w_filt_coeff[2] *= my;
w_filt_coeff[3] *= my;
w_filt_coeff[4] *= my;
w_filt_coeff[5] *= my;
w_filt_coeff[6] *= my;
w_filt_coeff[7] *= my;
w_filt_coeff += 8;
}
for(j=0; j<ord_residual; j++) // residual
w_filt_coeff[j] *= my;
}
}
}
}
rw_index++;
if(rw_index >= n_order)
rw_index -= n_order;
}
x->x_rw_index = rw_index; // back to start
if(compressed)
clock_delay(x->x_clock, 0);
return(w+3);
n_CNLMS_tildeperfzero:
err_out = x->x_err_out_ptr_beg;
for(i=0; i<n; i++)
*err_out++ = 0.0f;
for(j=0; j<n_io; j++)
{
filt_out = x->x_my_kern[j].x_out_ptr_beg;
for(i=0; i<n; i++)
*filt_out++ = 0.0f;
}
return(w+3);
}
static void n_CNLMS_tilde_dsp(t_n_CNLMS_tilde *x, t_signal **sp)
{
t_int i, n = sp[0]->s_n;
t_int ok_w = 1;
t_int m = x->x_n_io;
for(i=0; i<m; i++)
x->x_my_kern[i].x_in_ptr_beg = sp[i]->s_vec;
x->x_des_in_ptr_beg = sp[m]->s_vec;
for(i=0; i<m; i++)
x->x_my_kern[i].x_out_ptr_beg = sp[i+m+1]->s_vec;
x->x_err_out_ptr_beg = sp[2*m+1]->s_vec;
for(i=0; i<m; i++)
{
x->x_my_kern[i].x_w_array_mem_beg = n_CNLMS_tilde_check_array(x->x_my_kern[i].x_w_array_sym_name, x->x_n_order);
if(!x->x_my_kern[i].x_w_array_mem_beg)
ok_w = 0;
}
if(!ok_w)
dsp_add(n_CNLMS_tilde_perform_zero, 2, x, n);
else
dsp_add(n_CNLMS_tilde_perform, 2, x, n);
}
/* setup/setdown things */
static void n_CNLMS_tilde_free(t_n_CNLMS_tilde *x)
{
t_int i, n_io=x->x_n_io, n_order=x->x_n_order;
for(i=0; i<n_io; i++)
freebytes(x->x_my_kern[i].x_in_hist, 2*x->x_n_order*sizeof(t_float));
freebytes(x->x_my_kern, n_io*sizeof(t_n_CNLMS_tilde_kern));
clock_free(x->x_clock);
}
static void *n_CNLMS_tilde_new(t_symbol *s, t_int argc, t_atom *argv)
{
t_n_CNLMS_tilde *x = (t_n_CNLMS_tilde *)pd_new(n_CNLMS_tilde_class);
char buffer[400];
t_int i, n_order=39, n_io=1;
t_symbol *w_name;
t_float beta=0.1f;
t_float gamma=0.00001f;
t_float kappa = 1.0f;
if((argc >= 6) &&
IS_A_FLOAT(argv,0) && //IS_A_FLOAT/SYMBOL from iemlib.h
IS_A_FLOAT(argv,1) &&
IS_A_FLOAT(argv,2) &&
IS_A_FLOAT(argv,3) &&
IS_A_FLOAT(argv,4) &&
IS_A_SYMBOL(argv,5))
{
n_io = (t_int)atom_getintarg(0, argc, argv);
n_order = (t_int)atom_getintarg(1, argc, argv);
beta = (t_float)atom_getfloatarg(2, argc, argv);
gamma = (t_float)atom_getfloatarg(3, argc, argv);
kappa = (t_float)atom_getfloatarg(4, argc, argv);
w_name = (t_symbol *)atom_getsymbolarg(5, argc, argv);
if(beta < 0.0f)
beta = 0.0f;
if(beta > 2.0f)
beta = 2.0f;
if(gamma < 0.0f)
gamma = 0.0f;
if(gamma > 1.0f)
gamma = 1.0f;
if(kappa < 0.0001f)
kappa = 0.0001f;
if(kappa > 10000.0f)
kappa = 10000.0f;
if(n_order < 2)
n_order = 2;
if(n_order > 11111)
n_order = 11111;
if(n_io < 1)
n_io = 1;
if(n_io > 60)
n_io = 60;
for(i=0; i<n_io; i++)
inlet_new(&x->x_obj, &x->x_obj.ob_pd, &s_signal, &s_signal);
for(i=0; i<=n_io; i++)
outlet_new(&x->x_obj, &s_signal);
x->x_out_compressing_bang = outlet_new(&x->x_obj, &s_bang);
x->x_msi = 0;
x->x_n_io = n_io;
x->x_n_order = n_order;
x->x_update = 0;
x->x_beta = beta;
x->x_gamma = gamma;
x->x_kappa = kappa;
x->x_my_kern = (t_n_CNLMS_tilde_kern *)getbytes(x->x_n_io*sizeof(t_n_CNLMS_tilde_kern));
for(i=0; i<n_io; i++)
{
sprintf(buffer, "%d_%s", i+1, w_name->s_name);
x->x_my_kern[i].x_w_array_sym_name = gensym(buffer);
x->x_my_kern[i].x_w_array_mem_beg = (t_float *)0;
x->x_my_kern[i].x_in_hist = (t_float *)getbytes(2*x->x_n_order*sizeof(t_float));
}
x->x_clock = clock_new(x, (t_method)n_CNLMS_tilde_tick);
return(x);
}
else
{
post("n_CNLMSC~-ERROR: need 5 float- + 1 symbol-arguments:");
post(" number_of_filters + order_of_filters + learnrate_beta + security_value_gamma + threshold_kappa + array_name_taps");
return(0);
}
}
void n_CNLMS_tilde_setup(void)
{
n_CNLMS_tilde_class = class_new(gensym("n_CNLMS~"), (t_newmethod)n_CNLMS_tilde_new, (t_method)n_CNLMS_tilde_free,
sizeof(t_n_CNLMS_tilde), 0, A_GIMME, 0);
CLASS_MAINSIGNALIN(n_CNLMS_tilde_class, t_n_CNLMS_tilde, x_msi);
class_addmethod(n_CNLMS_tilde_class, (t_method)n_CNLMS_tilde_dsp, gensym("dsp"), 0);
class_addmethod(n_CNLMS_tilde_class, (t_method)n_CNLMS_tilde_update, gensym("update"), A_FLOAT, 0); // method: downsampling factor of learning (multiple of 2^N)
class_addmethod(n_CNLMS_tilde_class, (t_method)n_CNLMS_tilde_beta, gensym("beta"), A_FLOAT, 0); //method: normalized learning rate
class_addmethod(n_CNLMS_tilde_class, (t_method)n_CNLMS_tilde_gamma, gensym("gamma"), A_FLOAT, 0); // method: dithering noise related to signal
class_addmethod(n_CNLMS_tilde_class, (t_method)n_CNLMS_tilde_kappa, gensym("kappa"), A_FLOAT, 0); // method: threshold for compressing w_coeff
//class_sethelpsymbol(n_CNLMS_tilde_class, gensym("iemhelp2/n_CNLMS~"));
}
--- NEW FILE: n_CLNLMS~.c ---
/* For information on usage and redistribution, and for a DISCLAIMER OF ALL
* WARRANTIES, see the file, "LICENSE.txt," in this distribution.
n_CLNLMS multichannel-constrained leaky normalized LMS algorithm
lib iem_adaptfilt written by Markus Noisternig & Thomas Musil
noisternig_AT_iem.at; musil_AT_iem.at
(c) Institute of Electronic Music and Acoustics, Graz Austria 2005 */
#ifdef NT
#pragma warning( disable : 4244 )
#pragma warning( disable : 4305 )
#endif
#include "m_pd.h"
#include "iemlib.h"
#include <math.h>
#include <stdio.h>
#include <string.h>
/* ----------------------- n_CLNLMS~ ------------------------------ */
/* -- multiple Constraint LEAKY Normalized Least Mean Square (linear adaptive FIR-filter) -- */
//* -- first input: reference signal -- */
/* -- second input: desired signal -- */
/* -- -- */
/* for further information on adaptive filter design we refer to */
/* [1] Haykin, "Adaptive Filter Theory", 4th ed, Prentice Hall */
/* [2] Benesty, "Adaptive Signal Processing", Springer */
typedef struct n_CLNLMS_tilde_kern
{
t_symbol *x_w_array_sym_name;
t_float *x_w_array_mem_beg;
t_float *x_in_ptr_beg;// memory: sig-in vector
t_float *x_out_ptr_beg;// memory: sig-out vector
t_float *x_in_hist;// start point double buffer for sig-in history
} t_n_CLNLMS_tilde_kern;
typedef struct n_CLNLMS_tilde
{
t_object x_obj;
t_n_CLNLMS_tilde_kern *x_my_kern;
t_float *x_des_in_ptr_beg;// memory: desired-in vector
t_float *x_err_out_ptr_beg;// memory: error-out vector
t_int x_n_io;// number of in-channels and filtered out-channels
t_int x_rw_index;// read-write-index
t_int x_n_order;// filter order
t_int x_update;// rounded by 2^n, yields downsampling of learn-rate
t_float x_beta;// learn rate [0 .. 2]
t_float x_gamma;// normalization
t_float x_kappa;// constreint: treshold of energy (clipping)
t_float x_leakage;// leakage-Faktor for NLMS
t_outlet *x_out_compressing_bang;
t_clock *x_clock;
t_float x_msi;
} t_n_CLNLMS_tilde;
t_class *n_CLNLMS_tilde_class;
static void n_CLNLMS_tilde_tick(t_n_CLNLMS_tilde *x)
{
outlet_bang(x->x_out_compressing_bang);
}
static t_float *n_CLNLMS_tilde_check_array(t_symbol *array_sym_name, t_int length)
{
t_int n_points;
t_garray *a;
t_float *vec;
if(!(a = (t_garray *)pd_findbyclass(array_sym_name, garray_class)))
{
error("%s: no such array for n_CLNLMS~", array_sym_name->s_name);
return((t_float *)0);
}
else if(!garray_getfloatarray(a, &n_points, &vec))
{
error("%s: bad template for n_CLNLMS~", array_sym_name->s_name);
return((t_float *)0);
}
else if(n_points < length)
{
error("%s: bad array-size for n_CLNLMS~: %d", array_sym_name->s_name, n_points);
return((t_float *)0);
}
else
{
return(vec);
}
}
static void n_CLNLMS_tilde_beta(t_n_CLNLMS_tilde *x, t_floatarg f) // learn rate
{
if(f < 0.0f)
f = 0.0f;
if(f > 2.0f)
f = 2.0f;
x->x_beta = f;
}
static void n_CLNLMS_tilde_gamma(t_n_CLNLMS_tilde *x, t_floatarg f) // regularization (dither)
{
if(f < 0.0f)
f = 0.0f;
if(f > 1.0f)
f = 1.0f;
x->x_gamma = f;
}
static void n_CLNLMS_tilde_kappa(t_n_CLNLMS_tilde *x, t_floatarg f) // threshold for w_coeff
{
if(f < 0.0001f)
f = 0.0001f;
if(f > 10000.0f)
f = 10000.0f;
x->x_kappa = f;
}
static void n_CLNLMS_tilde_leakage(t_n_CLNLMS_tilde *x, t_floatarg f) // leakage of NLMS
{
if(f < 0.0001f)
f = 0.0001f;
if(f > 1.0f)
f = 1.0f;
x->x_leakage = f;
}
static void n_CLNLMS_tilde_update(t_n_CLNLMS_tilde *x, t_floatarg f) // downsample learn rate
{
t_int i=1, u = (t_int)f;
if(u < 0)
u = 0;
else
{
while(i <= u) // convert u for 2^N
i *= 2; // round down
i /= 2;
u = i;
}
x->x_update = u;
}
/* ============== DSP ======================= */
static t_int *n_CLNLMS_tilde_perform_zero(t_int *w)
{
t_n_CLNLMS_tilde *x = (t_n_CLNLMS_tilde *)(w[1]);
t_int n = (t_int)(w[2]);
t_int n_io = x->x_n_io;
t_float *out;
t_int i, j;
out = x->x_err_out_ptr_beg;
for(i=0; i<n; i++)
*out++ = 0.0f;
for(j=0; j<n_io; j++)
{
out = x->x_my_kern[j].x_out_ptr_beg;
for(i=0; i<n; i++)
*out++ = 0.0f;
}
return (w+3);
}
static t_int *n_CLNLMS_tilde_perform(t_int *w)
{
t_n_CLNLMS_tilde *x = (t_n_CLNLMS_tilde *)(w[1]);
t_int n = (t_int)(w[2]);
t_int n_order = x->x_n_order; /* number of filter-order */
t_int rw_index2, rw_index = x->x_rw_index;
t_int n_io = x->x_n_io;
t_float *in;// first sig in
t_float din;// second sig in
t_float *filt_out;// first sig out
t_float *err_out, err_sum;// second sig out
t_float *read_in_hist;
t_float *w_filt_coeff;
t_float my, my_err, sum;
t_float beta = x->x_beta;
t_float hgamma, gamma = x->x_gamma;
t_float hkappa, kappa = x->x_kappa;
t_float hleakage, leakage = x->x_leakage;
t_int i, j, k, update_counter;
t_int update = x->x_update;
t_int ord8=n_order&0xfffffff8;
t_int ord_residual=n_order&0x7;
t_int compressed = 0;
for(k=0; k<n_io; k++)
{
if(!x->x_my_kern[k].x_w_array_mem_beg)
goto n_CLNLMS_tildeperfzero;// this is Musil/Miller style
}
hgamma = gamma * gamma * (float)n_order;
//hkappa = kappa * kappa * (float)n_order;
hkappa = kappa; // kappa regards to energy value, else use line above
for(i=0, update_counter=0; i<n; i++)// history and (block-)convolution
{
rw_index2 = rw_index + n_order;
for(k=0; k<n_io; k++)// times n_io
{
x->x_my_kern[k].x_in_hist[rw_index] = x->x_my_kern[k].x_in_ptr_beg[i]; // save inputs into variabel & history
x->x_my_kern[k].x_in_hist[rw_index+n_order] = x->x_my_kern[k].x_in_ptr_beg[i];
}
din = x->x_des_in_ptr_beg[i];
// begin convolution
err_sum = din;
for(k=0; k<n_io; k++)// times n_io
{
sum = 0.0f;
w_filt_coeff = x->x_my_kern[k].x_w_array_mem_beg; // Musil's special convolution buffer struct
read_in_hist = &x->x_my_kern[k].x_in_hist[rw_index2];
for(j=0; j<ord8; j+=8) // loop unroll 8 taps
{
sum += w_filt_coeff[0] * read_in_hist[0];
sum += w_filt_coeff[1] * read_in_hist[-1];
sum += w_filt_coeff[2] * read_in_hist[-2];
sum += w_filt_coeff[3] * read_in_hist[-3];
sum += w_filt_coeff[4] * read_in_hist[-4];
sum += w_filt_coeff[5] * read_in_hist[-5];
sum += w_filt_coeff[6] * read_in_hist[-6];
sum += w_filt_coeff[7] * read_in_hist[-7];
w_filt_coeff += 8;
read_in_hist -= 8;
}
for(j=0; j<ord_residual; j++) // for filter order < 2^N
sum += w_filt_coeff[j] * read_in_hist[-j];
x->x_my_kern[k].x_out_ptr_beg[i] = sum;
err_sum -= sum;
}
x->x_err_out_ptr_beg[i] = err_sum;
// end convolution
if(update) // downsampling of learn rate
{
update_counter++;
if(update_counter >= update)
{
update_counter = 0;
for(k=0; k<n_io; k++)// times n_io
{
sum = 0.0f;// calculate energy for last n-order samples in filter
read_in_hist = &x->x_my_kern[k].x_in_hist[rw_index2];
for(j=0; j<ord8; j+=8) // unrolling quadrature calc
{
sum += read_in_hist[0] * read_in_hist[0];
sum += read_in_hist[-1] * read_in_hist[-1];
sum += read_in_hist[-2] * read_in_hist[-2];
sum += read_in_hist[-3] * read_in_hist[-3];
sum += read_in_hist[-4] * read_in_hist[-4];
sum += read_in_hist[-5] * read_in_hist[-5];
sum += read_in_hist[-6] * read_in_hist[-6];
sum += read_in_hist[-7] * read_in_hist[-7];
read_in_hist -= 8;
}
for(j=0; j<ord_residual; j++) // residual
sum += read_in_hist[-j] * read_in_hist[-j]; // [-j] only valid for Musil's double buffer structure
sum += hgamma; // convert gamma corresponding to filter order
my = beta / sum;// calculate mue
my_err = my * err_sum;
w_filt_coeff = x->x_my_kern[k].x_w_array_mem_beg;
read_in_hist = &x->x_my_kern[k].x_in_hist[rw_index2];
sum = 0.0f;
for(j=0; j<ord8; j+=8) // unrolling quadrature calc
{
w_filt_coeff[0] = leakage * w_filt_coeff[0] + read_in_hist[0] * my_err;
sum += w_filt_coeff[0] * w_filt_coeff[0];
w_filt_coeff[1] = leakage * w_filt_coeff[1] + read_in_hist[-1] * my_err;
sum += w_filt_coeff[1] * w_filt_coeff[1];
w_filt_coeff[2] = leakage * w_filt_coeff[2] + read_in_hist[-2] * my_err;
sum += w_filt_coeff[2] * w_filt_coeff[2];
w_filt_coeff[3] = leakage * w_filt_coeff[3] + read_in_hist[-3] * my_err;
sum += w_filt_coeff[3] * w_filt_coeff[3];
w_filt_coeff[4] = leakage * w_filt_coeff[4] + read_in_hist[-4] * my_err;
sum += w_filt_coeff[4] * w_filt_coeff[4];
w_filt_coeff[5] = leakage * w_filt_coeff[5] + read_in_hist[-5] * my_err;
sum += w_filt_coeff[5] * w_filt_coeff[5];
w_filt_coeff[6] = leakage * w_filt_coeff[6] + read_in_hist[-6] * my_err;
sum += w_filt_coeff[6] * w_filt_coeff[6];
w_filt_coeff[7] = leakage * w_filt_coeff[7] + read_in_hist[-7] * my_err;
sum += w_filt_coeff[7] * w_filt_coeff[7];
w_filt_coeff += 8;
read_in_hist -= 8;
}
for(j=0; j<ord_residual; j++) // residual
{
w_filt_coeff[j] = leakage * w_filt_coeff[j] + read_in_hist[-j] * my_err;
sum += w_filt_coeff[j] * w_filt_coeff[j];
}
if(sum > hkappa)
{
compressed = 1;
my = sqrt(hkappa/sum);
w_filt_coeff = x->x_my_kern[k].x_w_array_mem_beg;
for(j=0; j<ord8; j+=8) // unrolling quadrature calc
{
w_filt_coeff[0] *= my;
w_filt_coeff[1] *= my;
w_filt_coeff[2] *= my;
w_filt_coeff[3] *= my;
w_filt_coeff[4] *= my;
w_filt_coeff[5] *= my;
w_filt_coeff[6] *= my;
w_filt_coeff[7] *= my;
w_filt_coeff += 8;
}
for(j=0; j<ord_residual; j++) // residual
w_filt_coeff[j] *= my;
}
}
}
}
rw_index++;
if(rw_index >= n_order)
rw_index -= n_order;
}
x->x_rw_index = rw_index; // wieder in die garage stellen
if(compressed)
clock_delay(x->x_clock, 0);
return(w+3);
n_CLNLMS_tildeperfzero:
err_out = x->x_err_out_ptr_beg;
for(i=0; i<n; i++)
*err_out++ = 0.0f;
for(j=0; j<n_io; j++)
{
filt_out = x->x_my_kern[j].x_out_ptr_beg;
for(i=0; i<n; i++)
*filt_out++ = 0.0f;
}
return(w+3);
}
static void n_CLNLMS_tilde_dsp(t_n_CLNLMS_tilde *x, t_signal **sp)
{
t_int i, n = sp[0]->s_n;
t_int ok_w = 1;
t_int m = x->x_n_io;
for(i=0; i<m; i++)
x->x_my_kern[i].x_in_ptr_beg = sp[i]->s_vec;
x->x_des_in_ptr_beg = sp[m]->s_vec;
for(i=0; i<m; i++)
x->x_my_kern[i].x_out_ptr_beg = sp[i+m+1]->s_vec;
x->x_err_out_ptr_beg = sp[2*m+1]->s_vec;
for(i=0; i<m; i++)
{
x->x_my_kern[i].x_w_array_mem_beg = n_CLNLMS_tilde_check_array(x->x_my_kern[i].x_w_array_sym_name, x->x_n_order);
if(!x->x_my_kern[i].x_w_array_mem_beg)
ok_w = 0;
}
if(!ok_w)
dsp_add(n_CLNLMS_tilde_perform_zero, 2, x, n);
else
dsp_add(n_CLNLMS_tilde_perform, 2, x, n);
}
/* setup/setdown things */
static void n_CLNLMS_tilde_free(t_n_CLNLMS_tilde *x)
{
t_int i, n_io=x->x_n_io, n_order=x->x_n_order;
for(i=0; i<n_io; i++)
freebytes(x->x_my_kern[i].x_in_hist, 2*x->x_n_order*sizeof(t_float));
freebytes(x->x_my_kern, n_io*sizeof(t_n_CLNLMS_tilde_kern));
clock_free(x->x_clock);
}
static void *n_CLNLMS_tilde_new(t_symbol *s, t_int argc, t_atom *argv)
{
t_n_CLNLMS_tilde *x = (t_n_CLNLMS_tilde *)pd_new(n_CLNLMS_tilde_class);
char buffer[400];
t_int i, n_order=39, n_io=1;
t_symbol *w_name;
t_float beta=0.1f;
t_float gamma=0.00001f;
t_float kappa = 1.0f;
t_float leakage = 0.99f;
if((argc >= 7) &&
IS_A_FLOAT(argv,0) && //IS_A_FLOAT/SYMBOL from iemlib.h
IS_A_FLOAT(argv,1) &&
IS_A_FLOAT(argv,2) &&
IS_A_FLOAT(argv,3) &&
IS_A_FLOAT(argv,4) &&
IS_A_FLOAT(argv,5) &&
IS_A_SYMBOL(argv,6))
{
n_io = (t_int)atom_getintarg(0, argc, argv);
n_order = (t_int)atom_getintarg(1, argc, argv);
beta = (t_float)atom_getfloatarg(2, argc, argv);
gamma = (t_float)atom_getfloatarg(3, argc, argv);
kappa = (t_float)atom_getfloatarg(4, argc, argv);
leakage = (t_float)atom_getfloatarg(5, argc, argv);
w_name = (t_symbol *)atom_getsymbolarg(6, argc, argv);
if(beta < 0.0f)
beta = 0.0f;
if(beta > 2.0f)
beta = 2.0f;
if(gamma < 0.0f)
gamma = 0.0f;
if(gamma > 1.0f)
gamma = 1.0f;
if(kappa < 0.0001f)
kappa = 0.0001f;
if(kappa > 10000.0f)
kappa = 10000.0f;
if(leakage < 0.0001f)
leakage = 0.0001f;
if(leakage > 1.0f)
leakage = 1.0f;
if(n_order < 2)
n_order = 2;
if(n_order > 11111)
n_order = 11111;
if(n_io < 1)
n_io = 1;
if(n_io > 60)
n_io = 60;
for(i=0; i<n_io; i++)
inlet_new(&x->x_obj, &x->x_obj.ob_pd, &s_signal, &s_signal);
for(i=0; i<=n_io; i++)
outlet_new(&x->x_obj, &s_signal);
x->x_out_compressing_bang = outlet_new(&x->x_obj, &s_bang);
x->x_msi = 0;
x->x_n_io = n_io;
x->x_n_order = n_order;
x->x_update = 0;
x->x_beta = beta;
x->x_gamma = gamma;
x->x_kappa = kappa;
x->x_leakage = leakage;
x->x_my_kern = (t_n_CLNLMS_tilde_kern *)getbytes(x->x_n_io*sizeof(t_n_CLNLMS_tilde_kern));
for(i=0; i<n_io; i++)
{
sprintf(buffer, "%d_%s", i+1, w_name->s_name);
x->x_my_kern[i].x_w_array_sym_name = gensym(buffer);
x->x_my_kern[i].x_w_array_mem_beg = (t_float *)0;
x->x_my_kern[i].x_in_hist = (t_float *)getbytes(2*x->x_n_order*sizeof(t_float));
}
x->x_clock = clock_new(x, (t_method)n_CLNLMS_tilde_tick);
return(x);
}
else
{
post("n_CLNLMSC~-ERROR: need 6 float- + 1 symbol-arguments:");
post(" number_of_filters + order_of_filters + learnrate_beta + security_value_gamma + threshold_kappa + leakage_factor_lambda + array_name_taps");
return(0);
}
}
void n_CLNLMS_tilde_setup(void)
{
n_CLNLMS_tilde_class = class_new(gensym("n_CLNLMS~"), (t_newmethod)n_CLNLMS_tilde_new, (t_method)n_CLNLMS_tilde_free,
sizeof(t_n_CLNLMS_tilde), 0, A_GIMME, 0);
CLASS_MAINSIGNALIN(n_CLNLMS_tilde_class, t_n_CLNLMS_tilde, x_msi);
class_addmethod(n_CLNLMS_tilde_class, (t_method)n_CLNLMS_tilde_dsp, gensym("dsp"), 0);
class_addmethod(n_CLNLMS_tilde_class, (t_method)n_CLNLMS_tilde_update, gensym("update"), A_FLOAT, 0); // method: downsampling factor of learning (multiple of 2^N)
class_addmethod(n_CLNLMS_tilde_class, (t_method)n_CLNLMS_tilde_beta, gensym("beta"), A_FLOAT, 0); //method: normalized learning rate
class_addmethod(n_CLNLMS_tilde_class, (t_method)n_CLNLMS_tilde_gamma, gensym("gamma"), A_FLOAT, 0); // method: dithering noise related to signal
class_addmethod(n_CLNLMS_tilde_class, (t_method)n_CLNLMS_tilde_kappa, gensym("kappa"), A_FLOAT, 0); // method: threshold for compressing w_coeff
class_addmethod(n_CLNLMS_tilde_class, (t_method)n_CLNLMS_tilde_leakage, gensym("leakage"), A_FLOAT, 0); // method: leakage factor [0 1] for w update
//class_sethelpsymbol(n_CLNLMS_tilde_class, gensym("iemhelp2/n_CLNLMS~"));
}
Index: makefile_win
===================================================================
RCS file: /cvsroot/pure-data/externals/iem/iem_adaptfilt/src/makefile_win,v
retrieving revision 1.1
retrieving revision 1.2
diff -C2 -d -r1.1 -r1.2
*** makefile_win 2 Aug 2006 14:02:28 -0000 1.1
--- makefile_win 11 Jan 2007 17:40:10 -0000 1.2
***************
*** 22,29 ****
! SRC = sigNLMS.c \
! sigNLMSCC.c \
! sign_CNLMS.c \
! sign_CLNLMS.c \
iem_adaptfilt.c
--- 22,29 ----
! SRC = NLMS~.c \
! NLMSCC~.c \
! n_CNLMS~.c \
! n_CLNLMS~.c \
iem_adaptfilt.c
--- NEW FILE: NLMS~.c ---
/* For information on usage and redistribution, and for a DISCLAIMER OF ALL
* WARRANTIES, see the file, "LICENSE.txt," in this distribution.
NLMS normalized least mean square (LMS) algorithm
lib iem_adaptfilt written by Markus Noisternig & Thomas Musil
noisternig_AT_iem.at; musil_AT_iem.at
(c) Institute of Electronic Music and Acoustics, Graz Austria 2005 */
#ifdef NT
#pragma warning( disable : 4244 )
#pragma warning( disable : 4305 )
#endif
#include "m_pd.h"
#include "iemlib.h"
#include <math.h>
#include <stdio.h>
#include <string.h>
/* ----------------------- NLMS~ ------------------------------ */
/* -- Normalized Least Mean Square (linear adaptive FIR-filter) -- */
/* -- first input: reference signal -- */
/* -- second input: desired signal -- */
/* -- -- */
/* for further information on adaptive filter design we refer to */
/* [1] Haykin, "Adaptive Filter Theory", 4th ed, Prentice Hall */
/* [2] Benesty, "Adaptive Signal Processing", Springer */
typedef struct NLMS_tilde
{
t_object x_obj;
t_symbol *x_w_array_sym_name;
t_float *x_w_array_mem_beg;
t_float *x_io_ptr_beg[4];// memory: 2 sig-in and 2 sig-out vectors
t_float *x_in_hist;// start point double buffer for sig-in history
t_int x_rw_index;// read-write-index
t_int x_n_order;// order of filter
t_int x_update;// 2^n rounded value, downsampling of update speed
t_float x_beta;// learn rate [0 .. 2]
t_float x_gamma;// regularization
t_float x_msi;
} t_NLMS_tilde;
t_class *NLMS_tilde_class;
static t_float *NLMS_tilde_check_array(t_symbol *array_sym_name, t_int length)
{
t_int n_points;
t_garray *a;
t_float *vec;
if(!(a = (t_garray *)pd_findbyclass(array_sym_name, garray_class)))
{
error("%s: no such array for NLMS~", array_sym_name->s_name);
return((t_float *)0);
}
else if(!garray_getfloatarray(a, &n_points, &vec))
{
error("%s: bad template for NLMS~", array_sym_name->s_name);
return((t_float *)0);
}
else if(n_points < length)
{
error("%s: bad array-size for NLMS~: %d", array_sym_name->s_name, n_points);
return((t_float *)0);
}
else
{
return(vec);
}
}
static void NLMS_tilde_beta(t_NLMS_tilde *x, t_floatarg f) // learn rate
{
if(f < 0.0f)
f = 0.0f;
if(f > 2.0f)
f = 2.0f;
x->x_beta = f;
}
static void NLMS_tilde_gamma(t_NLMS_tilde *x, t_floatarg f) // regularization factor (dither)
{
if(f < 0.0f)
f = 0.0f;
if(f > 1.0f)
f = 1.0f;
x->x_gamma = f;
}
static void NLMS_tilde_update(t_NLMS_tilde *x, t_floatarg f) // downsample learn-rate
{
t_int i=1, u = (t_int)f;
if(u < 0)
u = 0;
else
{
while(i <= u) // convert u for 2^N
i *= 2; // round downwards
i /= 2;
u = i;
}
x->x_update = u;
}
/* ============== DSP ======================= */
static t_int *NLMS_tilde_perform_zero(t_int *w)
{
t_NLMS_tilde *x = (t_NLMS_tilde *)(w[1]);
t_int n = (t_int)(w[2]);
t_float **io = x->x_io_ptr_beg;
t_float *out;
t_int i, j;
for(j=0; j<2; j++)/* output-vector-row */
{
out = io[j+2];
for(i=0; i<n; i++)
{
*out++ = 0.0f;
}
}
return (w+3);
}
static t_int *NLMS_tilde_perform(t_int *w)
{
t_NLMS_tilde *x = (t_NLMS_tilde *)(w[1]);
t_int n = (t_int)(w[2]);
t_int n_order = x->x_n_order; /* number of filter-order */
t_int rw_index = x->x_rw_index;
t_float *in = x->x_io_ptr_beg[0];// first sig in
t_float *desired_in = x->x_io_ptr_beg[1], din;// second sig in
t_float *filt_out = x->x_io_ptr_beg[2];// first sig out
t_float *err_out = x->x_io_ptr_beg[3], eout;// second sig out
t_float *write_in_hist1 = x->x_in_hist;
t_float *write_in_hist2 = write_in_hist1+n_order;
t_float *read_in_hist = write_in_hist2;
t_float *w_filt_coeff = x->x_w_array_mem_beg;
t_float my, my_err, sum;
t_float beta = x->x_beta;
t_float gamma = x->x_gamma;
t_int i, j, update_counter;
t_int update = x->x_update;
t_int ord8=n_order&0xfffffff8;
t_int ord_residual=n_order&0x7;
if(!w_filt_coeff)
goto NLMS_tildeperfzero;// this is quick&dirty Musil/Miller style
for(i=0, update_counter=0; i<n; i++)// store history and convolve
{
write_in_hist1[rw_index] = in[i]; // save inputs to variable & history
write_in_hist2[rw_index] = in[i];
din = desired_in[i];
// begin convolution
sum = 0.0f;
w_filt_coeff = x->x_w_array_mem_beg; // Musil's special convolution buffer struct
read_in_hist = &write_in_hist2[rw_index];
for(j=0; j<ord8; j+=8) // loop unroll 8 taps
{
sum += w_filt_coeff[0] * read_in_hist[0];
sum += w_filt_coeff[1] * read_in_hist[-1];
sum += w_filt_coeff[2] * read_in_hist[-2];
sum += w_filt_coeff[3] * read_in_hist[-3];
sum += w_filt_coeff[4] * read_in_hist[-4];
sum += w_filt_coeff[5] * read_in_hist[-5];
sum += w_filt_coeff[6] * read_in_hist[-6];
sum += w_filt_coeff[7] * read_in_hist[-7];
w_filt_coeff += 8;
read_in_hist -= 8;
}
for(j=0; j<ord_residual; j++) // for filter order < 2^N
sum += w_filt_coeff[j] * read_in_hist[-j];
filt_out[i] = sum;
eout = din - filt_out[i]; // buffer-struct for further use
err_out[i] = eout;
if(update) // downsampling for learn rate
{
update_counter++;
if(update_counter >= update)
{
update_counter = 0;
sum = 0.0f;// calculate energy for last n-order samples in filter
read_in_hist = &write_in_hist2[rw_index];
for(j=0; j<ord8; j+=8) // unrolling quadrature calc
{
sum += read_in_hist[0] * read_in_hist[0];
sum += read_in_hist[-1] * read_in_hist[-1];
sum += read_in_hist[-2] * read_in_hist[-2];
sum += read_in_hist[-3] * read_in_hist[-3];
sum += read_in_hist[-4] * read_in_hist[-4];
sum += read_in_hist[-5] * read_in_hist[-5];
sum += read_in_hist[-6] * read_in_hist[-6];
sum += read_in_hist[-7] * read_in_hist[-7];
read_in_hist -= 8;
}
for(j=0; j<ord_residual; j++) // residual
sum += read_in_hist[-j] * read_in_hist[-j]; // [-j] only valid for Musil's double buffer structure
sum += gamma * gamma * (float)n_order; // convert gamma corresponding to filter order
my = beta / sum;// calculate mue
my_err = my * eout;
w_filt_coeff = x->x_w_array_mem_beg; // coefficient constraints
read_in_hist = &write_in_hist2[rw_index];
for(j=0; j<n_order; j++) // without unroll
w_filt_coeff[j] += read_in_hist[-j] * my_err;
}
}
rw_index++;
if(rw_index >= n_order)
rw_index -= n_order;
}
x->x_rw_index = rw_index; // back to start
return(w+3);
NLMS_tildeperfzero:
while(n--)
{
*filt_out++ = 0.0f;
*err_out++ = 0.0f;
}
return(w+3);
}
static void NLMS_tilde_dsp(t_NLMS_tilde *x, t_signal **sp)
{
t_int i, n = sp[0]->s_n;
for(i=0; i<4; i++) // store io_vec
x->x_io_ptr_beg[i] = sp[i]->s_vec;
x->x_w_array_mem_beg = NLMS_tilde_check_array(x->x_w_array_sym_name, x->x_n_order);
if(!x->x_w_array_mem_beg)
dsp_add(NLMS_tilde_perform_zero, 2, x, n);
else
dsp_add(NLMS_tilde_perform, 2, x, n);
}
/* setup/setdown things */
static void NLMS_tilde_free(t_NLMS_tilde *x)
{
freebytes(x->x_in_hist, 2*x->x_n_order*sizeof(t_float));
}
static void *NLMS_tilde_new(t_symbol *s, t_int argc, t_atom *argv)
{
t_NLMS_tilde *x = (t_NLMS_tilde *)pd_new(NLMS_tilde_class);
t_int i, n_order=39;
t_symbol *w_name;
t_float beta=0.1f;
t_float gamma=0.00001f;
if((argc >= 4) &&
IS_A_FLOAT(argv,0) && //IS_A_FLOAT/SYMBOL from iemlib.h
IS_A_FLOAT(argv,1) &&
IS_A_FLOAT(argv,2) &&
IS_A_SYMBOL(argv,3))
{
n_order = (t_int)atom_getintarg(0, argc, argv);
beta = (t_float)atom_getfloatarg(1, argc, argv);
gamma = (t_float)atom_getfloatarg(2, argc, argv);
w_name = (t_symbol *)atom_getsymbolarg(3, argc, argv);
if(beta < 0.0f)
beta = 0.0f;
if(beta > 2.0f)
beta = 2.0f;
if(gamma < 0.0f)
gamma = 0.0f;
if(gamma > 1.0f)
gamma = 1.0f;
if(n_order < 2)
n_order = 2;
if(n_order > 11111)
n_order = 11111;
inlet_new(&x->x_obj, &x->x_obj.ob_pd, &s_signal, &s_signal);
outlet_new(&x->x_obj, &s_signal);
outlet_new(&x->x_obj, &s_signal);
x->x_msi = 0;
x->x_n_order = n_order;
x->x_update = 0;
x->x_beta = beta;
x->x_gamma = gamma;
// 2 times in and one time desired_in memory allocation (history)
x->x_in_hist = (t_float *)getbytes(2*x->x_n_order*sizeof(t_float));
// table-symbols will be linked to their memory in future (dsp_routine)
x->x_w_array_sym_name = gensym(w_name->s_name);
x->x_w_array_mem_beg = (t_float *)0;
return(x);
}
else
{
post("NLMS~-ERROR: need 3 float- + 1 symbol-arguments:");
post(" order_of_filter + learnrate_beta + security_value + array_name_taps");
return(0);
}
}
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), 0, A_GIMME, 0);
CLASS_MAINSIGNALIN(NLMS_tilde_class, t_NLMS_tilde, x_msi);
class_addmethod(NLMS_tilde_class, (t_method)NLMS_tilde_dsp, gensym("dsp"), 0);
class_addmethod(NLMS_tilde_class, (t_method)NLMS_tilde_update, gensym("update"), A_FLOAT, 0); // method: downsampling factor of learning (multiple of 2^N)
class_addmethod(NLMS_tilde_class, (t_method)NLMS_tilde_beta, gensym("beta"), A_FLOAT, 0); //method: normalized learning rate
class_addmethod(NLMS_tilde_class, (t_method)NLMS_tilde_gamma, gensym("gamma"), A_FLOAT, 0); // method: dithering noise related to signal
//class_sethelpsymbol(NLMS_tilde_class, gensym("iemhelp2/NLMS~"));
}
--- NEW FILE: NLMSCC~.c ---
/* For information on usage and redistribution, and for a DISCLAIMER OF ALL
* WARRANTIES, see the file, "LICENSE.txt," in this distribution.
NLMSCC normalized LMS algorithm with coefficient constraints
lib iem_adaptfilt written by Markus Noisternig & Thomas Musil
noisternig_AT_iem.at; musil_AT_iem.at
(c) Institute of Electronic Music and Acoustics, Graz Austria 2005 */
#ifdef NT
#pragma warning( disable : 4244 )
#pragma warning( disable : 4305 )
#endif
#include "m_pd.h"
#include "iemlib.h"
#include <math.h>
#include <stdio.h>
#include <string.h>
/* ----------------------- NLMSCC~ ------------------------------ */
/* -- Normalized Least Mean Square (linear adaptive FIR-filter) -- */
/* -- with Coefficient Constraint
/* -- first input: reference signal -- */
/* -- second input: desired signal -- */
/* -- -- */
/* for further information on adaptive filter design we refer to */
/* [1] Haykin, "Adaptive Filter Theory", 4th ed, Prentice Hall */
/* [2] Benesty, "Adaptive Signal Processing", Springer */
/* */
typedef struct NLMSCC_tilde
{
t_object x_obj;
t_symbol *x_w_array_sym_name;
t_float *x_w_array_mem_beg;
t_symbol *x_wmin_array_sym_name;
t_float *x_wmin_array_mem_beg;
t_symbol *x_wmax_array_sym_name;
t_float *x_wmax_array_mem_beg;
t_float *x_io_ptr_beg[4];// memory: 2 sig-in and 2 sig-out vectors
t_float *x_in_hist;// start point double buffer for sig-in history
t_int x_rw_index;// read-write-index
t_int x_n_order;// order of filter
t_int x_update;// 2^n rounded value, downsampling of update speed
t_float x_beta;// learn rate [0 .. 2]
t_float x_gamma;// regularization
t_outlet *x_out_clipping_bang;
t_clock *x_clock;
t_float x_msi;
} t_NLMSCC_tilde;
t_class *NLMSCC_tilde_class;
static void NLMSCC_tilde_tick(t_NLMSCC_tilde *x)
{
outlet_bang(x->x_out_clipping_bang);
}
static t_float *NLMSCC_tilde_check_array(t_symbol *array_sym_name, t_int length)
{
t_int n_points;
t_garray *a;
t_float *vec;
if(!(a = (t_garray *)pd_findbyclass(array_sym_name, garray_class)))
{
error("%s: no such array for NLMSCC~", array_sym_name->s_name);
return((t_float *)0);
}
else if(!garray_getfloatarray(a, &n_points, &vec))
{
error("%s: bad template for NLMSCC~", array_sym_name->s_name);
return((t_float *)0);
}
else if(n_points < length)
{
error("%s: bad array-size for NLMSCC~: %d", array_sym_name->s_name, n_points);
return((t_float *)0);
}
else
{
return(vec);
}
}
static void NLMSCC_tilde_beta(t_NLMSCC_tilde *x, t_floatarg f) // learn rate
{
if(f < 0.0f)
f = 0.0f;
if(f > 2.0f)
f = 2.0f;
x->x_beta = f;
}
static void NLMSCC_tilde_gamma(t_NLMSCC_tilde *x, t_floatarg f) // regularization factor (dither)
{
if(f < 0.0f)
f = 0.0f;
if(f > 1.0f)
f = 1.0f;
x->x_gamma = f;
}
static void NLMSCC_tilde_update(t_NLMSCC_tilde *x, t_floatarg f) // downsample of learn-rate
{
t_int i=1, u = (t_int)f;
if(u < 0)
u = 0;
else
{
while(i <= u) // convert u for 2^N
i *= 2; // round downwards
i /= 2;
u = i;
}
x->x_update = u;
}
/* ============== DSP ======================= */
static t_int *NLMSCC_tilde_perform_zero(t_int *w)
{
t_NLMSCC_tilde *x = (t_NLMSCC_tilde *)(w[1]);
t_int n = (t_int)(w[2]);
t_float **io = x->x_io_ptr_beg;
t_float *out;
t_int i, j;
for(j=0; j<2; j++)/* output-vector-row */
{
out = io[j+2];
for(i=0; i<n; i++)
{
*out++ = 0.0f;
}
}
return (w+3);
}
static t_int *NLMSCC_tilde_perform(t_int *w)
{
t_NLMSCC_tilde *x = (t_NLMSCC_tilde *)(w[1]);
t_int n = (t_int)(w[2]);
t_int n_order = x->x_n_order; /* filter-order */
t_int rw_index = x->x_rw_index;
t_float *in = x->x_io_ptr_beg[0];// first sig in
t_float *desired_in = x->x_io_ptr_beg[1], din;// second sig in
t_float *filt_out = x->x_io_ptr_beg[2];// first sig out
t_float *err_out = x->x_io_ptr_beg[3], eout;// second sig out
t_float *write_in_hist1 = x->x_in_hist;
t_float *write_in_hist2 = write_in_hist1+n_order;
t_float *read_in_hist = write_in_hist2;
t_float *w_filt_coeff = x->x_w_array_mem_beg;
t_float *wmin_filt_coeff = x->x_wmin_array_mem_beg;
t_float *wmax_filt_coeff = x->x_wmax_array_mem_beg;
t_float my, my_err, sum;
t_float beta = x->x_beta;
t_float gamma = x->x_gamma;
t_int i, j, update_counter;
t_int update = x->x_update;
t_int ord8=n_order&0xfffffff8;
t_int ord_residual=n_order&0x7;
t_int clipped = 0;
if(!w_filt_coeff)
goto NLMSCC_tildeperfzero;// this is Musil/Miller style
if(!wmin_filt_coeff)
goto NLMSCC_tildeperfzero;
if(!wmax_filt_coeff)
goto NLMSCC_tildeperfzero;// if not constrained, perform zero
for(i=0, update_counter=0; i<n; i++)// store in history and convolve
{
write_in_hist1[rw_index] = in[i]; // save inputs into variabel & history
write_in_hist2[rw_index] = in[i];
din = desired_in[i];
// begin convolution
sum = 0.0f;
w_filt_coeff = x->x_w_array_mem_beg; // Musil's special convolution buffer struct
read_in_hist = &write_in_hist2[rw_index];
for(j=0; j<ord8; j+=8) // loop unroll 8 taps
{
sum += w_filt_coeff[0] * read_in_hist[0];
sum += w_filt_coeff[1] * read_in_hist[-1];
sum += w_filt_coeff[2] * read_in_hist[-2];
sum += w_filt_coeff[3] * read_in_hist[-3];
sum += w_filt_coeff[4] * read_in_hist[-4];
sum += w_filt_coeff[5] * read_in_hist[-5];
sum += w_filt_coeff[6] * read_in_hist[-6];
sum += w_filt_coeff[7] * read_in_hist[-7];
w_filt_coeff += 8;
read_in_hist -= 8;
}
for(j=0; j<ord_residual; j++) // for filter order < 2^N
sum += w_filt_coeff[j] * read_in_hist[-j];
filt_out[i] = sum;
eout = din - filt_out[i]; // buffer-struct for further use
err_out[i] = eout;
if(update) // downsampling for learn rate
{
update_counter++;
if(update_counter >= update)
{
update_counter = 0;
sum = 0.0f;// calculate energy for last n-order samples in filter
read_in_hist = &write_in_hist2[rw_index];
for(j=0; j<ord8; j+=8) // unrolling quadrature calc
{
sum += read_in_hist[0] * read_in_hist[0];
sum += read_in_hist[-1] * read_in_hist[-1];
sum += read_in_hist[-2] * read_in_hist[-2];
sum += read_in_hist[-3] * read_in_hist[-3];
sum += read_in_hist[-4] * read_in_hist[-4];
sum += read_in_hist[-5] * read_in_hist[-5];
sum += read_in_hist[-6] * read_in_hist[-6];
sum += read_in_hist[-7] * read_in_hist[-7];
read_in_hist -= 8;
}
for(j=0; j<ord_residual; j++) // residual
sum += read_in_hist[-j] * read_in_hist[-j]; // [-j] only valid for Musil's double buffer structure
sum += gamma * gamma * (float)n_order; // convert gamma corresponding to filter order
my = beta / sum;// calculate mue
my_err = my * eout;
w_filt_coeff = x->x_w_array_mem_beg; // coefficient constraints
wmin_filt_coeff = x->x_wmin_array_mem_beg;
wmax_filt_coeff = x->x_wmax_array_mem_beg;
read_in_hist = &write_in_hist2[rw_index];
for(j=0; j<n_order; j++) // without unroll
{
w_filt_coeff[j] += read_in_hist[-j] * my_err;
if(w_filt_coeff[j] > wmax_filt_coeff[j])
{
w_filt_coeff[j] = wmax_filt_coeff[j];
clipped = 1;
}
else if(w_filt_coeff[j] < wmin_filt_coeff[j])
{
w_filt_coeff[j] = wmin_filt_coeff[j];
clipped = 1;
}
}
}
}
rw_index++;
if(rw_index >= n_order)
rw_index -= n_order;
}
x->x_rw_index = rw_index; // back to start
if(clipped)
clock_delay(x->x_clock, 0);
return(w+3);
NLMSCC_tildeperfzero:
while(n--)
{
*filt_out++ = 0.0f;
*err_out++ = 0.0f;
}
return(w+3);
}
static void NLMSCC_tilde_dsp(t_NLMSCC_tilde *x, t_signal **sp)
{
t_int i, n = sp[0]->s_n;
for(i=0; i<4; i++) // store io_vec
x->x_io_ptr_beg[i] = sp[i]->s_vec;
x->x_w_array_mem_beg = NLMSCC_tilde_check_array(x->x_w_array_sym_name, x->x_n_order);
x->x_wmin_array_mem_beg = NLMSCC_tilde_check_array(x->x_wmin_array_sym_name, x->x_n_order);
x->x_wmax_array_mem_beg = NLMSCC_tilde_check_array(x->x_wmax_array_sym_name, x->x_n_order);
if(!(x->x_w_array_mem_beg && x->x_wmin_array_mem_beg && x->x_wmax_array_mem_beg))
dsp_add(NLMSCC_tilde_perform_zero, 2, x, n);
else
dsp_add(NLMSCC_tilde_perform, 2, x, n);
}
/* setup/setdown things */
static void NLMSCC_tilde_free(t_NLMSCC_tilde *x)
{
freebytes(x->x_in_hist, 2*x->x_n_order*sizeof(t_float));
clock_free(x->x_clock);
}
static void *NLMSCC_tilde_new(t_symbol *s, t_int argc, t_atom *argv)
{
t_NLMSCC_tilde *x = (t_NLMSCC_tilde *)pd_new(NLMSCC_tilde_class);
t_int i, n_order=39;
t_symbol *w_name;
t_symbol *wmin_name;
t_symbol *wmax_name;
t_float beta=0.1f;
t_float gamma=0.00001f;
if((argc >= 6) &&
IS_A_FLOAT(argv,0) && //IS_A_FLOAT/SYMBOL from iemlib.h
IS_A_FLOAT(argv,1) &&
IS_A_FLOAT(argv,2) &&
IS_A_SYMBOL(argv,3) &&
IS_A_SYMBOL(argv,4) &&
IS_A_SYMBOL(argv,5))
{
n_order = (t_int)atom_getintarg(0, argc, argv);
beta = (t_float)atom_getfloatarg(1, argc, argv);
gamma = (t_float)atom_getfloatarg(2, argc, argv);
w_name = (t_symbol *)atom_getsymbolarg(3, argc, argv);
wmin_name = (t_symbol *)atom_getsymbolarg(4, argc, argv);
wmax_name = (t_symbol *)atom_getsymbolarg(5, argc, argv);
if(beta < 0.0f)
beta = 0.0f;
if(beta > 2.0f)
beta = 2.0f;
if(gamma < 0.0f)
gamma = 0.0f;
if(gamma > 1.0f)
gamma = 1.0f;
if(n_order < 2)
n_order = 2;
if(n_order > 11111)
n_order = 11111;
inlet_new(&x->x_obj, &x->x_obj.ob_pd, &s_signal, &s_signal);
outlet_new(&x->x_obj, &s_signal);
outlet_new(&x->x_obj, &s_signal);
x->x_out_clipping_bang = outlet_new(&x->x_obj, &s_bang);
x->x_msi = 0;
x->x_n_order = n_order;
x->x_update = 0;
x->x_beta = beta;
x->x_gamma = gamma;
// 2 times in and one time desired_in memory allocation (history)
x->x_in_hist = (t_float *)getbytes(2*x->x_n_order*sizeof(t_float));
// table-symbols will be linked to their memory in future (dsp_routine)
x->x_w_array_sym_name = gensym(w_name->s_name);
x->x_w_array_mem_beg = (t_float *)0;
x->x_wmin_array_sym_name = gensym(wmin_name->s_name);
x->x_wmin_array_mem_beg = (t_float *)0;
x->x_wmax_array_sym_name = gensym(wmax_name->s_name);
x->x_wmax_array_mem_beg = (t_float *)0;
x->x_clock = clock_new(x, (t_method)NLMSCC_tilde_tick);
return(x);
}
else
{
post("NLMSCC~-ERROR: need 3 float- + 3 symbol-arguments:");
post(" order_of_filter + learnrate_beta + security_value + array_name_taps + array_name_tap_min + array_name_tap_max");
return(0);
}
}
void NLMSCC_tilde_setup(void)
{
NLMSCC_tilde_class = class_new(gensym("NLMSCC~"), (t_newmethod)NLMSCC_tilde_new, (t_method)NLMSCC_tilde_free,
sizeof(t_NLMSCC_tilde), 0, A_GIMME, 0);
CLASS_MAINSIGNALIN(NLMSCC_tilde_class, t_NLMSCC_tilde, x_msi);
class_addmethod(NLMSCC_tilde_class, (t_method)NLMSCC_tilde_dsp, gensym("dsp"), 0);
class_addmethod(NLMSCC_tilde_class, (t_method)NLMSCC_tilde_update, gensym("update"), A_FLOAT, 0); // method: downsampling factor of learning (multiple of 2^N)
class_addmethod(NLMSCC_tilde_class, (t_method)NLMSCC_tilde_beta, gensym("beta"), A_FLOAT, 0); //method: normalized learning rate
class_addmethod(NLMSCC_tilde_class, (t_method)NLMSCC_tilde_gamma, gensym("gamma"), A_FLOAT, 0); // method: dithering noise related to signal
//class_sethelpsymbol(NLMSCC_tilde_class, gensym("iemhelp2/NLMSCC~"));
}
Index: makefile.txt
===================================================================
RCS file: /cvsroot/pure-data/externals/iem/iem_adaptfilt/src/makefile.txt,v
retrieving revision 1.1
retrieving revision 1.2
diff -C2 -d -r1.1 -r1.2
*** makefile.txt 2 Aug 2006 14:02:28 -0000 1.1
--- makefile.txt 11 Jan 2007 17:40:10 -0000 1.2
***************
*** 18,24 ****
# the sources
! SRC = sigNLMS.c \
! sigNLMSCC.c \
! sign_CNLMS.c \
iem_adaptfilt.c
--- 18,24 ----
# the sources
! SRC = NLMS~.c \
! NLMSCC~.c \
! n_CNLMS~.c \
iem_adaptfilt.c
Index: iem_adaptfilt.c
===================================================================
RCS file: /cvsroot/pure-data/externals/iem/iem_adaptfilt/src/iem_adaptfilt.c,v
retrieving revision 1.1
retrieving revision 1.2
diff -C2 -d -r1.1 -r1.2
*** iem_adaptfilt.c 2 Aug 2006 14:02:28 -0000 1.1
--- iem_adaptfilt.c 11 Jan 2007 17:40:10 -0000 1.2
***************
*** 24,31 ****
}
! void sigNLMS_setup(void);
! void sigNLMSCC_setup(void);
! void sign_CNLMS_setup(void);
! void sign_CLNLMS_setup(void);
/* ------------------------ setup routine ------------------------- */
--- 24,31 ----
}
! void NLMS_tilde_setup(void);
! void NLMSCC_tilde_setup(void);
! void n_CNLMS_tilde_setup(void);
! void n_CLNLMS_tilde_setup(void);
/* ------------------------ setup routine ------------------------- */
***************
*** 33,40 ****
void iem_adaptfilt_setup(void)
{
! sigNLMS_setup();
! sigNLMSCC_setup();
! sign_CNLMS_setup();
! sign_CLNLMS_setup();
post("----------------------------------------------");
--- 33,40 ----
void iem_adaptfilt_setup(void)
{
! NLMS_tilde_setup();
! NLMSCC_tilde_setup();
! n_CNLMS_tilde_setup();
! n_CLNLMS_tilde_setup();
post("----------------------------------------------");
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