[PD-cvs] externals/iem/iem_adaptfilt/src .DS_Store, NONE, 1.1 iem_adaptfilt.c, NONE, 1.1 iemlib.h, NONE, 1.1 makefile.txt, NONE, 1.1 makefile_lin, NONE, 1.1 makefile_win, NONE, 1.1 makefile_win.txt, NONE, 1.1 sign_CLNLMS.c, NONE, 1.1 sign_CNLMS.c, NONE, 1.1 sigNLMS.c, NONE, 1.1 sigNLMSCC.c, NONE, 1.1
Markus Noisternig
mnoi at users.sourceforge.net
Wed Aug 2 16:02:31 CEST 2006
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Update of /cvsroot/pure-data/externals/iem/iem_adaptfilt/src
In directory sc8-pr-cvs1.sourceforge.net:/tmp/cvs-serv13932/externals/iem/iem_adaptfilt/src
Added Files:
.DS_Store iem_adaptfilt.c iemlib.h makefile.txt makefile_lin
makefile_win makefile_win.txt sign_CLNLMS.c sign_CNLMS.c
sigNLMS.c sigNLMSCC.c
Log Message:
no message
--- NEW FILE: makefile_lin ---
current: all
.SUFFIXES: .pd_linux
INCLUDE = -I. -I/usr/local/src/pd-0.37-1/src
LDFLAGS = -export-dynamic -shared
LIB = -ldl -lm -lpthread
#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
SYSTEM = $(shell uname -m)
# the sources
SRC = sigNLMS.c \
sigNLMSCC.c \
sign_CNLMS.c \
sign_CLNLMS.c \
iem_adaptfilt.c
TARGET = iem_adaptfilt.pd_linux
OBJ = $(SRC:.c=.o)
#
# ------------------ targets ------------------------------------
#
clean:
rm ../../lib/$(TARGET)
rm *.o
all: $(OBJ)
@echo :: $(OBJ)
ld $(LDFLAGS) -o $(TARGET) *.o $(LIB)
strip --strip-unneeded $(TARGET)
rm *.o
$(OBJ) : %.o : %.c
touch $*.c
cc $(CFLAGS) $(INCLUDE) -c -o $*.o $*.c
--- NEW FILE: makefile_win.txt ---
all: iem_adaptfilt.dll
VIS_CPP_PATH = "C:\Programme\Microsoft Visual Studio\Vc98"
PD_INST_PATH = "C:\Programme\pd-0.37-1"
PD_WIN_INCLUDE_PATH = /I. /I$(PD_INST_PATH)\src /I$(VIS_CPP_PATH)\include
PD_WIN_C_FLAGS = /nologo /W3 /WX /DMSW /DNT /DPD /DWIN32 /DWINDOWS /Ox -DPA_LITTLE_ENDIAN
PD_WIN_L_FLAGS = /nologo
PD_WIN_LIB = /NODEFAULTLIB:libc /NODEFAULTLIB:oldnames /NODEFAULTLIB:kernel /NODEFAULTLIB:uuid \
$(VIS_CPP_PATH)\lib\libc.lib \
$(VIS_CPP_PATH)\lib\oldnames.lib \
$(VIS_CPP_PATH)\lib\kernel32.lib \
$(VIS_CPP_PATH)\lib\wsock32.lib \
$(VIS_CPP_PATH)\lib\winmm.lib \
$(PD_INST_PATH)\bin\pthreadVC.lib \
$(PD_INST_PATH)\bin\pd.lib
SRC = sigNLMS.c \
sigNLMSCC.c \
sign_CNLMS.c \
sign_CLNLMS.c \
iem_adaptfilt.c
OBJ = $(SRC:.c=.obj)
.c.obj:
cl $(PD_WIN_C_FLAGS) $(PD_WIN_INCLUDE_PATH) /c $*.c
iem_adaptfilt.dll: $(OBJ)
link $(PD_WIN_L_FLAGS) /dll /export:iem_adaptfilt_setup \
/out:iem_adaptfilt.dll $(OBJ) $(PD_WIN_LIB)
copy iem_adaptfilt.dll ..\..\lib\iem_adaptfilt.dll
copy iem_adaptfilt.dll ..\..\..\iem_adaptfilt.dll
clean:
del *.obj
--- NEW FILE: sign_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 sign_CLNLMS_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_sign_CLNLMS_kern;
typedef struct sign_CLNLMS
{
t_object x_obj;
t_sign_CLNLMS_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_sign_CLNLMS;
t_class *sign_CLNLMS_class;
static void sign_CLNLMS_tick(t_sign_CLNLMS *x)
{
outlet_bang(x->x_out_compressing_bang);
}
static t_float *sign_CLNLMS_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 sign_CLNLMS_beta(t_sign_CLNLMS *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 sign_CLNLMS_gamma(t_sign_CLNLMS *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 sign_CLNLMS_kappa(t_sign_CLNLMS *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 sign_CLNLMS_leakage(t_sign_CLNLMS *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 sign_CLNLMS_update(t_sign_CLNLMS *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 *sign_CLNLMS_perform_zero(t_int *w)
{
t_sign_CLNLMS *x = (t_sign_CLNLMS *)(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 *sign_CLNLMS_perform(t_int *w)
{
t_sign_CLNLMS *x = (t_sign_CLNLMS *)(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 sign_CLNLMSperfzero;// 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);
sign_CLNLMSperfzero:
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 sign_CLNLMS_dsp(t_sign_CLNLMS *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 = sign_CLNLMS_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(sign_CLNLMS_perform_zero, 2, x, n);
else
dsp_add(sign_CLNLMS_perform, 2, x, n);
}
/* setup/setdown things */
static void sign_CLNLMS_free(t_sign_CLNLMS *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_sign_CLNLMS_kern));
clock_free(x->x_clock);
}
static void *sign_CLNLMS_new(t_symbol *s, t_int argc, t_atom *argv)
{
t_sign_CLNLMS *x = (t_sign_CLNLMS *)pd_new(sign_CLNLMS_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_sign_CLNLMS_kern *)getbytes(x->x_n_io*sizeof(t_sign_CLNLMS_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)sign_CLNLMS_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 sign_CLNLMS_setup(void)
{
sign_CLNLMS_class = class_new(gensym("n_CLNLMS~"), (t_newmethod)sign_CLNLMS_new, (t_method)sign_CLNLMS_free,
sizeof(t_sign_CLNLMS), 0, A_GIMME, 0);
CLASS_MAINSIGNALIN(sign_CLNLMS_class, t_sign_CLNLMS, x_msi);
class_addmethod(sign_CLNLMS_class, (t_method)sign_CLNLMS_dsp, gensym("dsp"), 0);
class_addmethod(sign_CLNLMS_class, (t_method)sign_CLNLMS_update, gensym("update"), A_FLOAT, 0); // method: downsampling factor of learning (multiple of 2^N)
class_addmethod(sign_CLNLMS_class, (t_method)sign_CLNLMS_beta, gensym("beta"), A_FLOAT, 0); //method: normalized learning rate
class_addmethod(sign_CLNLMS_class, (t_method)sign_CLNLMS_gamma, gensym("gamma"), A_FLOAT, 0); // method: dithering noise related to signal
class_addmethod(sign_CLNLMS_class, (t_method)sign_CLNLMS_kappa, gensym("kappa"), A_FLOAT, 0); // method: threshold for compressing w_coeff
class_addmethod(sign_CLNLMS_class, (t_method)sign_CLNLMS_leakage, gensym("leakage"), A_FLOAT, 0); // method: leakage factor [0 1] for w update
class_sethelpsymbol(sign_CLNLMS_class, gensym("iemhelp2/n_CLNLMS~"));
}
--- NEW FILE: sigNLMSCC.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 sigNLMSCC
{
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_sigNLMSCC;
t_class *sigNLMSCC_class;
static void sigNLMSCC_tick(t_sigNLMSCC *x)
{
outlet_bang(x->x_out_clipping_bang);
}
static t_float *sigNLMSCC_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 sigNLMSCC_beta(t_sigNLMSCC *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 sigNLMSCC_gamma(t_sigNLMSCC *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 sigNLMSCC_update(t_sigNLMSCC *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 *sigNLMSCC_perform_zero(t_int *w)
{
t_sigNLMSCC *x = (t_sigNLMSCC *)(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 *sigNLMSCC_perform(t_int *w)
{
t_sigNLMSCC *x = (t_sigNLMSCC *)(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 sigNLMSCCperfzero;// this is Musil/Miller style
if(!wmin_filt_coeff)
goto sigNLMSCCperfzero;
if(!wmax_filt_coeff)
goto sigNLMSCCperfzero;// 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);
sigNLMSCCperfzero:
while(n--)
{
*filt_out++ = 0.0f;
*err_out++ = 0.0f;
}
return(w+3);
}
static void sigNLMSCC_dsp(t_sigNLMSCC *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 = sigNLMSCC_check_array(x->x_w_array_sym_name, x->x_n_order);
x->x_wmin_array_mem_beg = sigNLMSCC_check_array(x->x_wmin_array_sym_name, x->x_n_order);
x->x_wmax_array_mem_beg = sigNLMSCC_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(sigNLMSCC_perform_zero, 2, x, n);
else
dsp_add(sigNLMSCC_perform, 2, x, n);
}
/* setup/setdown things */
static void sigNLMSCC_free(t_sigNLMSCC *x)
{
freebytes(x->x_in_hist, 2*x->x_n_order*sizeof(t_float));
clock_free(x->x_clock);
}
static void *sigNLMSCC_new(t_symbol *s, t_int argc, t_atom *argv)
{
t_sigNLMSCC *x = (t_sigNLMSCC *)pd_new(sigNLMSCC_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)sigNLMSCC_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 sigNLMSCC_setup(void)
{
sigNLMSCC_class = class_new(gensym("NLMSCC~"), (t_newmethod)sigNLMSCC_new, (t_method)sigNLMSCC_free,
sizeof(t_sigNLMSCC), 0, A_GIMME, 0);
CLASS_MAINSIGNALIN(sigNLMSCC_class, t_sigNLMSCC, x_msi);
class_addmethod(sigNLMSCC_class, (t_method)sigNLMSCC_dsp, gensym("dsp"), 0);
class_addmethod(sigNLMSCC_class, (t_method)sigNLMSCC_update, gensym("update"), A_FLOAT, 0); // method: downsampling factor of learning (multiple of 2^N)
class_addmethod(sigNLMSCC_class, (t_method)sigNLMSCC_beta, gensym("beta"), A_FLOAT, 0); //method: normalized learning rate
class_addmethod(sigNLMSCC_class, (t_method)sigNLMSCC_gamma, gensym("gamma"), A_FLOAT, 0); // method: dithering noise related to signal
class_sethelpsymbol(sigNLMSCC_class, gensym("iemhelp2/NLMSCC~"));
}
--- NEW FILE: makefile_win ---
all: iem_adaptfilt.dll
VIS_CPP_PATH = "C:\Programme\Microsoft Visual Studio\Vc98"
PD_INST_PATH = "C:\Programme\pd-0.37-3"
PD_WIN_INCLUDE_PATH = /I. /I$(PD_INST_PATH)\src /I$(VIS_CPP_PATH)\include
PD_WIN_C_FLAGS = /nologo /W3 /WX /DMSW /DNT /DPD /DWIN32 /DWINDOWS /Ox -DPA_LITTLE_ENDIAN
PD_WIN_L_FLAGS = /nologo
PD_WIN_LIB = /NODEFAULTLIB:libc /NODEFAULTLIB:oldnames /NODEFAULTLIB:kernel /NODEFAULTLIB:uuid \
$(VIS_CPP_PATH)\lib\libc.lib \
$(VIS_CPP_PATH)\lib\oldnames.lib \
$(VIS_CPP_PATH)\lib\kernel32.lib \
$(VIS_CPP_PATH)\lib\wsock32.lib \
$(VIS_CPP_PATH)\lib\winmm.lib \
$(PD_INST_PATH)\bin\pthreadVC.lib \
$(PD_INST_PATH)\bin\pd.lib
SRC = sigNLMS.c \
sigNLMSCC.c \
sign_CNLMS.c \
sign_CLNLMS.c \
iem_adaptfilt.c
OBJ = $(SRC:.c=.obj)
.c.obj:
cl $(PD_WIN_C_FLAGS) $(PD_WIN_INCLUDE_PATH) /c $*.c
iem_adaptfilt.dll: $(OBJ)
link $(PD_WIN_L_FLAGS) /dll /export:iem_adaptfilt_setup \
/out:iem_adaptfilt.dll $(OBJ) $(PD_WIN_LIB)
clean:
del *.obj
--- NEW FILE: sigNLMS.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 sigNLMS
{
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_sigNLMS;
t_class *sigNLMS_class;
static t_float *sigNLMS_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 sigNLMS_beta(t_sigNLMS *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 sigNLMS_gamma(t_sigNLMS *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 sigNLMS_update(t_sigNLMS *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 *sigNLMS_perform_zero(t_int *w)
{
t_sigNLMS *x = (t_sigNLMS *)(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 *sigNLMS_perform(t_int *w)
{
t_sigNLMS *x = (t_sigNLMS *)(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 sigNLMSperfzero;// 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);
sigNLMSperfzero:
while(n--)
{
*filt_out++ = 0.0f;
*err_out++ = 0.0f;
}
return(w+3);
}
static void sigNLMS_dsp(t_sigNLMS *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 = sigNLMS_check_array(x->x_w_array_sym_name, x->x_n_order);
if(!x->x_w_array_mem_beg)
dsp_add(sigNLMS_perform_zero, 2, x, n);
else
dsp_add(sigNLMS_perform, 2, x, n);
}
/* setup/setdown things */
static void sigNLMS_free(t_sigNLMS *x)
{
freebytes(x->x_in_hist, 2*x->x_n_order*sizeof(t_float));
}
static void *sigNLMS_new(t_symbol *s, t_int argc, t_atom *argv)
{
t_sigNLMS *x = (t_sigNLMS *)pd_new(sigNLMS_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 sigNLMS_setup(void)
{
sigNLMS_class = class_new(gensym("NLMS~"), (t_newmethod)sigNLMS_new, (t_method)sigNLMS_free,
sizeof(t_sigNLMS), 0, A_GIMME, 0);
CLASS_MAINSIGNALIN(sigNLMS_class, t_sigNLMS, x_msi);
class_addmethod(sigNLMS_class, (t_method)sigNLMS_dsp, gensym("dsp"), 0);
class_addmethod(sigNLMS_class, (t_method)sigNLMS_update, gensym("update"), A_FLOAT, 0); // method: downsampling factor of learning (multiple of 2^N)
class_addmethod(sigNLMS_class, (t_method)sigNLMS_beta, gensym("beta"), A_FLOAT, 0); //method: normalized learning rate
class_addmethod(sigNLMS_class, (t_method)sigNLMS_gamma, gensym("gamma"), A_FLOAT, 0); // method: dithering noise related to signal
class_sethelpsymbol(sigNLMS_class, gensym("iemhelp2/NLMS~"));
}
--- NEW FILE: makefile.txt ---
current: all
.SUFFIXES: .pd_linux
INCLUDE = -I. -I/usr/local/src/pd-0.37-1/src
LDFLAGS = -export-dynamic -shared
LIB = -ldl -lm -lpthread
#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
SYSTEM = $(shell uname -m)
# the sources
SRC = sigNLMS.c \
sigNLMSCC.c \
sign_CNLMS.c \
iem_adaptfilt.c
TARGET = iem_adaptfilt.pd_linux
OBJ = $(SRC:.c=.o)
#
# ------------------ targets ------------------------------------
#
clean:
rm $(TARGET)
rm *.o
all: $(OBJ)
@echo :: $(OBJ)
ld $(LDFLAGS) -o $(TARGET) *.o $(LIB)
strip --strip-unneeded $(TARGET)
rm *.o
$(OBJ) : %.o : %.c
touch $*.c
cc $(CFLAGS) $(INCLUDE) -c -o $*.o $*.c
--- NEW FILE: .DS_Store ---
(This appears to be a binary file; contents omitted.)
--- NEW FILE: sign_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 sign_CNLMS_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_sign_CNLMS_kern;
typedef struct sign_CNLMS
{
t_object x_obj;
t_sign_CNLMS_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_sign_CNLMS;
t_class *sign_CNLMS_class;
static void sign_CNLMS_tick(t_sign_CNLMS *x)
{
outlet_bang(x->x_out_compressing_bang);
}
static t_float *sign_CNLMS_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 sign_CNLMS_beta(t_sign_CNLMS *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 sign_CNLMS_gamma(t_sign_CNLMS *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 sign_CNLMS_kappa(t_sign_CNLMS *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 sign_CNLMS_update(t_sign_CNLMS *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 *sign_CNLMS_perform_zero(t_int *w)
{
t_sign_CNLMS *x = (t_sign_CNLMS *)(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 *sign_CNLMS_perform(t_int *w)
{
t_sign_CNLMS *x = (t_sign_CNLMS *)(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 sign_CNLMSperfzero;// 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);
sign_CNLMSperfzero:
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 sign_CNLMS_dsp(t_sign_CNLMS *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 = sign_CNLMS_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(sign_CNLMS_perform_zero, 2, x, n);
else
dsp_add(sign_CNLMS_perform, 2, x, n);
}
/* setup/setdown things */
static void sign_CNLMS_free(t_sign_CNLMS *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_sign_CNLMS_kern));
clock_free(x->x_clock);
}
static void *sign_CNLMS_new(t_symbol *s, t_int argc, t_atom *argv)
{
t_sign_CNLMS *x = (t_sign_CNLMS *)pd_new(sign_CNLMS_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_sign_CNLMS_kern *)getbytes(x->x_n_io*sizeof(t_sign_CNLMS_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)sign_CNLMS_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 sign_CNLMS_setup(void)
{
sign_CNLMS_class = class_new(gensym("n_CNLMS~"), (t_newmethod)sign_CNLMS_new, (t_method)sign_CNLMS_free,
sizeof(t_sign_CNLMS), 0, A_GIMME, 0);
CLASS_MAINSIGNALIN(sign_CNLMS_class, t_sign_CNLMS, x_msi);
class_addmethod(sign_CNLMS_class, (t_method)sign_CNLMS_dsp, gensym("dsp"), 0);
class_addmethod(sign_CNLMS_class, (t_method)sign_CNLMS_update, gensym("update"), A_FLOAT, 0); // method: downsampling factor of learning (multiple of 2^N)
class_addmethod(sign_CNLMS_class, (t_method)sign_CNLMS_beta, gensym("beta"), A_FLOAT, 0); //method: normalized learning rate
class_addmethod(sign_CNLMS_class, (t_method)sign_CNLMS_gamma, gensym("gamma"), A_FLOAT, 0); // method: dithering noise related to signal
class_addmethod(sign_CNLMS_class, (t_method)sign_CNLMS_kappa, gensym("kappa"), A_FLOAT, 0); // method: threshold for compressing w_coeff
class_sethelpsymbol(sign_CNLMS_class, gensym("iemhelp2/n_CNLMS~"));
}
--- NEW FILE: iem_adaptfilt.c ---
/* For information on usage and redistribution, and for a DISCLAIMER OF ALL
* WARRANTIES, see the file, "LICENSE.txt," in this distribution.
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"
static t_class *iem_adaptfilt_class;
static void *iem_adaptfilt_new(void)
{
t_object *x = (t_object *)pd_new(iem_adaptfilt_class);
return (x);
}
void sigNLMS_setup(void);
void sigNLMSCC_setup(void);
void sign_CNLMS_setup(void);
void sign_CLNLMS_setup(void);
/* ------------------------ setup routine ------------------------- */
void iem_adaptfilt_setup(void)
{
sigNLMS_setup();
sigNLMSCC_setup();
sign_CNLMS_setup();
sign_CLNLMS_setup();
post("----------------------------------------------");
post("iem_adaptfilt (R-1.02) library loaded!");
post("(c) Markus Noisternig, Thomas Musil");
post(" {noisternig, musil}_AT_iem.at");
post(" IEM Graz, Austria");
post("----------------------------------------------");
}
--- NEW FILE: iemlib.h ---
/* For information on usage and redistribution, and for a DISCLAIMER OF ALL
* WARRANTIES, see the file, "LICENSE.txt," in this distribution.
iemlib2 written by Thomas Musil, Copyright (c) IEM KUG Graz Austria 2000 - 2004 */
#ifndef __IEMLIB_H__
#define __IEMLIB_H__
#define IS_A_POINTER(atom,index) ((atom+index)->a_type == A_POINTER)
#define IS_A_FLOAT(atom,index) ((atom+index)->a_type == A_FLOAT)
#define IS_A_SYMBOL(atom,index) ((atom+index)->a_type == A_SYMBOL)
#define IS_A_DOLLAR(atom,index) ((atom+index)->a_type == A_DOLLAR)
#define IS_A_DOLLSYM(atom,index) ((atom+index)->a_type == A_DOLLSYM)
#define IS_A_SEMI(atom,index) ((atom+index)->a_type == A_SEMI)
#define IS_A_COMMA(atom,index) ((atom+index)->a_type == A_COMMA)
#ifdef NT
int sys_noloadbang;
//t_symbol *iemgui_key_sym=0;
#include <io.h>
#else
extern int sys_noloadbang;
//extern t_symbol *iemgui_key_sym;
#include <unistd.h>
#endif
#define DEFDELVS 64
#define XTRASAMPS 4
#define SAMPBLK 4
#define UNITBIT32 1572864. /* 3*2^19; bit 32 has place value 1 */
/* machine-dependent definitions. These ifdefs really
should have been by CPU type and not by operating system! */
#ifdef IRIX
/* big-endian. Most significant byte is at low address in memory */
#define HIOFFSET 0 /* word offset to find MSB */
#define LOWOFFSET 1 /* word offset to find LSB */
#define int32 long /* a data type that has 32 bits */
#else
#ifdef MSW
/* little-endian; most significant byte is at highest address */
#define HIOFFSET 1
#define LOWOFFSET 0
#define int32 long
#else
#ifdef __FreeBSD__
#include <machine/endian.h>
#if BYTE_ORDER == LITTLE_ENDIAN
#define HIOFFSET 1
#define LOWOFFSET 0
#else
#define HIOFFSET 0 /* word offset to find MSB */
#define LOWOFFSET 1 /* word offset to find LSB */
#endif /* BYTE_ORDER */
#include <sys/types.h>
#define int32 int32_t
#endif
#ifdef __linux__
#include <endian.h>
#if !defined(__BYTE_ORDER) || !defined(__LITTLE_ENDIAN)
#error No byte order defined
#endif
#if __BYTE_ORDER == __LITTLE_ENDIAN
#define HIOFFSET 1
#define LOWOFFSET 0
#else
#define HIOFFSET 0 /* word offset to find MSB */
#define LOWOFFSET 1 /* word offset to find LSB */
#endif /* __BYTE_ORDER */
#include <sys/types.h>
#define int32 int32_t
#else
#ifdef __APPLE__
#define HIOFFSET 0 /* word offset to find MSB */
#define LOWOFFSET 1 /* word offset to find LSB */
#define int32 int /* a data type that has 32 bits */
#endif /* __APPLE__ */
#endif /* __linux__ */
#endif /* MSW */
#endif /* SGI */
union tabfudge
{
double tf_d;
int32 tf_i[2];
};
#define IEM_DENORMAL(f) ((((*(unsigned int*)&(f))&0x60000000)==0) || \
(((*(unsigned int*)&(f))&0x60000000)==0x60000000))
/* more stringent test: anything not between 1e-19 and 1e19 in absolute val */
#endif
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