changed files to new naming convention

svn path=/trunk/externals/iem/iem_adaptfilt/; revision=7293

9 files changed, 1741 insertions, 25 deletions

diff --git a/src/NLMSCC~.c b/src/NLMSCC~.c
new file mode 100644
index 0000000..1dc4610
--- /dev/null
+++ b/src/NLMSCC~.c
@@ -0,0 +1,391 @@
+/* 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~"));
+}
diff --git a/src/NLMS~.c b/src/NLMS~.c
new file mode 100644
index 0000000..b065afa
--- /dev/null
+++ b/src/NLMS~.c
@@ -0,0 +1,338 @@
+/* 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~"));
+}
diff --git a/src/iem_adaptfilt.c b/src/iem_adaptfilt.c
index 11381ee..a70d12f 100644
--- a/src/iem_adaptfilt.c
+++ b/src/iem_adaptfilt.c
@@ -23,19 +23,19 @@ static void *iem_adaptfilt_new(void)
 	return (x);
 }
 
-void sigNLMS_setup(void);
-void sigNLMSCC_setup(void);
-void sign_CNLMS_setup(void);
-void sign_CLNLMS_setup(void);
+void NLMS_tilde_setup(void);
+void NLMSCC_tilde_setup(void);
+void n_CNLMS_tilde_setup(void);
+void n_CLNLMS_tilde_setup(void);
 
 /* ------------------------ setup routine ------------------------- */
 
 void iem_adaptfilt_setup(void)
 {
-  sigNLMS_setup();
-  sigNLMSCC_setup();
-  sign_CNLMS_setup();
-  sign_CLNLMS_setup();
+  NLMS_tilde_setup();
+  NLMSCC_tilde_setup();
+  n_CNLMS_tilde_setup();
+  n_CLNLMS_tilde_setup();
   	
   	post("----------------------------------------------");
 	post("iem_adaptfilt (R-1.02) library loaded!");
diff --git a/src/makefile.txt b/src/makefile.txt
index 6654843..699a5f2 100644
--- a/src/makefile.txt
+++ b/src/makefile.txt
@@ -17,9 +17,9 @@ SYSTEM = $(shell uname -m)
 
 # the sources
 
-SRC = sigNLMS.c \
-	sigNLMSCC.c \
-	sign_CNLMS.c \
+SRC = NLMS~.c \
+	NLMSCC~.c \
+	n_CNLMS~.c \
 	iem_adaptfilt.c
 
 TARGET = iem_adaptfilt.pd_linux
diff --git a/src/makefile_lin b/src/makefile_lin
index 1e6a625..6db08d8 100644
--- a/src/makefile_lin
+++ b/src/makefile_lin
@@ -2,14 +2,14 @@ current: all
 
 .SUFFIXES: .pd_linux
 
-INCLUDE = -I. -I/usr/local/src/pd-0.37-1/src
+INCLUDE = -I. -I/usr/local/src/pd/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 \
+CFLAGS = -DPD -DUNIX -W -Wno-unused \
 	-Wno-parentheses -Wno-switch -O6 -funroll-loops -fomit-frame-pointer \
         -DDL_OPEN
 
@@ -17,10 +17,10 @@ SYSTEM = $(shell uname -m)
 
 # the sources
 
-SRC = sigNLMS.c \
-	sigNLMSCC.c \
-	sign_CNLMS.c \
-	sign_CLNLMS.c \
+SRC = NLMS~.c \
+	NLMSCC~.c \
+	n_CNLMS~.c \
+	n_CLNLMS~.c \
 	iem_adaptfilt.c
 
 TARGET = iem_adaptfilt.pd_linux
diff --git a/src/makefile_win b/src/makefile_win
index fe10c99..69f09be 100644
--- a/src/makefile_win
+++ b/src/makefile_win
@@ -21,10 +21,10 @@ PD_WIN_LIB = /NODEFAULTLIB:libc /NODEFAULTLIB:oldnames /NODEFAULTLIB:kernel /NOD
 	$(PD_INST_PATH)\bin\pd.lib
 
 
-SRC =	sigNLMS.c \
-	sigNLMSCC.c \
-	sign_CNLMS.c \
-	sign_CLNLMS.c \
+SRC =	NLMS~.c \
+	NLMSCC~.c \
+	n_CNLMS~.c \
+	n_CLNLMS~.c \
 	iem_adaptfilt.c
 
 
diff --git a/src/makefile_win.txt b/src/makefile_win.txt
index bfbedf8..bd8077b 100644
--- a/src/makefile_win.txt
+++ b/src/makefile_win.txt
@@ -21,10 +21,10 @@ PD_WIN_LIB = /NODEFAULTLIB:libc /NODEFAULTLIB:oldnames /NODEFAULTLIB:kernel /NOD
 	$(PD_INST_PATH)\bin\pd.lib
 
 
-SRC =	sigNLMS.c \
-	sigNLMSCC.c \
-	sign_CNLMS.c \
-	sign_CLNLMS.c \
+SRC =	NLMS~.c \
+	NLMSCC~.c \
+	n_CNLMS~.c \
+	n_CLNLMS~.c \
 	iem_adaptfilt.c
 
 
diff --git a/src/n_CLNLMS~.c b/src/n_CLNLMS~.c
new file mode 100644
index 0000000..05ef1c1
--- /dev/null
+++ b/src/n_CLNLMS~.c
@@ -0,0 +1,504 @@
+/* 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~"));
+}
diff --git a/src/n_CNLMS~.c b/src/n_CNLMS~.c
new file mode 100644
index 0000000..b3b1389
--- /dev/null
+++ b/src/n_CNLMS~.c
@@ -0,0 +1,483 @@
+/* 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~"));
+}