sync iem_adaptfilt library with Pd-l2ork (must have missed a commit awhile back)

externals/iem/iem_adaptfilt/help/NLMSerr_in~-help.pd

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+#X text 712 267 (array-sizes have to be >= then FIR_size);
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+#X text 395 422 error signal;
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+#X text 126 301 minimum input value;
+#X msg 226 195 0.1;
+#X msg 65 281 1e-05;
+#X text 138 240 beta [0 .. 2];
+#X text 132 318 gamma [0 .. 1];
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+#X text 231 146 >;
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+#X text 232 138 _;
+#X text 283 141 2;
+#X text 346 141 2;
+#X text 311 146 gamma * blocksize);
+#X text 300 146 +;
+#X text 243 146 x[n-i];
+#X text 174 135 my(n) =;
+#X text 223 130 _________________________________;
+#X text 16 135 normalized learn rate:;
+#X text 173 56 y(n) = W * x(n);
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+#X text 119 73 error:;
+#X text 116 55 output:;
+#X text 26 210 coefficient iteration:;
+#X text 188 211 W(k+1 \, i) = W(k \, i) + my(n)* e(n)* x(n);
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+#X text 409 343 filt out;
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+#X text 41 118 Update ON / OFF;
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+#X text 660 364 (C) 2010 \, t.musil & m.guldenschuh \, IEM \, Graz
+\, Austria;
+#X text 688 377 [musil | guldenschuh]_AT_iem.at;
+#X text 662 173 Normalized Least Mean Square with separated error and
+adaption reference input (linear adaptive FIR-filter);
+#X obj 53 450 NLMSerr_in~ 100 0.003 1e-05 W;
+#X msg 114 282 1e-07;
+#X msg 85 195 0.0001;
+#X text 662 290 3.arg: <float> minimum input value gamma for;
+#X text 716 301 energy calculation;
+#X text 46 44 NLMSerr_in~;
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externals/iem/iem_adaptfilt/src/NLMSCC~.c

@@ -163,7 +163,7 @@ static t_int *NLMSCC_tilde_perform(t_int *w)
     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_float gammax = x->x_gamma;
     t_int i, j, update_counter;
     t_int update = x->x_update;
     t_int ord8=n_order&0xfffffff8;
@@ -230,7 +230,7 @@ static t_int *NLMSCC_tilde_perform(t_int *w)
                 }
                 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
+                sum += gammax * gammax * (float)n_order; // convert gammax corresponding to filter order
                 my = beta / sum;// calculate mue
                 
                 
@@ -312,7 +312,7 @@ static void *NLMSCC_tilde_new(t_symbol *s, t_int argc, t_atom *argv)
     t_symbol    *wmin_name;
     t_symbol    *wmax_name;
     t_float beta=0.1f;
-    t_float gamma=0.00001f;
+    t_float gammax=0.00001f;
     
     if((argc >= 6) &&
         IS_A_FLOAT(argv,0) &&   //IS_A_FLOAT/SYMBOL from iemlib.h
@@ -324,7 +324,7 @@ static void *NLMSCC_tilde_new(t_symbol *s, t_int argc, t_atom *argv)
     {
         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);
+        gammax  = (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);
@@ -334,10 +334,10 @@ static void *NLMSCC_tilde_new(t_symbol *s, t_int argc, t_atom *argv)
         if(beta > 2.0f)
             beta = 2.0f;
         
-        if(gamma < 0.0f)
-            gamma = 0.0f;
-        if(gamma > 1.0f)
-            gamma = 1.0f;
+        if(gammax < 0.0f)
+            gammax = 0.0f;
+        if(gammax > 1.0f)
+            gammax = 1.0f;
         
         if(n_order < 2)
             n_order = 2;
@@ -353,7 +353,7 @@ static void *NLMSCC_tilde_new(t_symbol *s, t_int argc, t_atom *argv)
         x->x_n_order = n_order;
         x->x_update = 0;
         x->x_beta = beta;
-        x->x_gamma = gamma;
+        x->x_gamma = gammax;
         // 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));
         
@@ -382,7 +382,7 @@ 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"), A_CANT, 0);
+    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

externals/iem/iem_adaptfilt/src/NLMSerr_in~.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 */
+
+
+#include "m_pd.h"
+#include "iemlib.h"
+#include <math.h>
+#include <stdio.h>
+#include <string.h>
+
+
+/* ----------------------- NLMSerr_in~ ------------------------------ */
+/* -- Normalized Least Mean Square (linear adaptive FIR-filter) -- */
+/* -- first input:  reference signal -- */
+/* -- second input: desired signal -- */
+/* -- the difference to NLMS~ is: we have only one ERROR input instead of desired in minus filter out  -- */
+/* -- that means there is no feedback  -- */
+
+/* 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 NLMSerr_in_tilde
+{
+    t_object            x_obj;// common pd object structure
+    t_symbol            *x_w_array_sym_name;
+    t_float             *x_w_array_mem_beg;
+    t_float             *x_ref_filt_in_hist;// history buffer for input signal reference for internal convolution = filter
+    t_float             *x_ref_adapt_in_hist;// history buffer for input signal reference for internal adapting filter
+    t_int               x_rw_index;// current read-write-index in circular buffer
+    t_int               x_n_order;// order of filter or convolution
+    t_int               x_update;// binary update parameter ON / OFF
+    t_float             x_beta;// learn rate [0 .. 2]
+    t_float             x_gamma;// regularization Parameter = minimum
+    t_float             x_flt_sig_in1;
+} t_NLMSerr_in_tilde;
+
+t_class *NLMSerr_in_tilde_class;
+
+static t_float *NLMSerr_in_tilde_check_array(t_symbol *array_sym_name, t_int length)
+{
+  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 NLMSerr_in~", array_sym_name->s_name);
+    return((t_float *)0);
+  }
+  else if(!garray_getfloatarray(a, &n_points, &vec))
+  {
+    error("%s: bad template for NLMSerr_in~", array_sym_name->s_name);
+    return((t_float *)0);
+  }
+  else if(n_points < length)
+  {
+    error("%s: bad array-size for NLMSerr_in~: %d", array_sym_name->s_name, n_points);
+    return((t_float *)0);
+  }
+  else
+  {
+    return(vec);
+  }
+}
+
+static void NLMSerr_in_tilde_beta(t_NLMSerr_in_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 NLMSerr_in_tilde_gamma(t_NLMSerr_in_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 NLMSerr_in_tilde_update(t_NLMSerr_in_tilde *x, t_floatarg f) // downsample learn-rate
+{
+  t_int i=1, u = (t_int)f;
+  
+  if(u != 0)
+    u = 1;
+  x->x_update = u;
+}
+
+/* ============== DSP ======================= */
+
+static t_int *NLMSerr_in_tilde_perform_zero(t_int *w)
+{
+  t_NLMSerr_in_tilde *x = (t_NLMSerr_in_tilde *)(w[5]);
+  t_int n = (t_int)(w[6]);
+  t_float *filt_out = (t_float *)(w[4]);
+  t_int i;
+  
+  for(i=0; i<n; i++)
+  {
+    *filt_out++ = 0.0f;
+  }
+  return (w+7);
+}
+
+static t_int *NLMSerr_in_tilde_perform(t_int *w)
+{
+  t_int n = (t_int)(w[6]);
+  t_NLMSerr_in_tilde *x = (t_NLMSerr_in_tilde *)(w[5]);
+  t_float *filt_out = (t_float *)(w[4]);// first sig out
+  t_float *err_in = (t_float *)(w[3]);// third sig in
+  t_float *ref_adapt_in = (t_float *)(w[2]);// second sig in
+  t_float *ref_filt_in = (t_float *)(w[1]);// first sig in
+  t_int n_order = x->x_n_order;   /* number of filter-order */
+  t_int rw_index = x->x_rw_index;  /* current read write index in circular buffer */
+  t_int update = x->x_update;
+  t_float beta = x->x_beta;  /* learn rate */
+  t_float gammax = x->x_gamma;  /* minimum energy */
+  t_float my, my_err, sum, errin;
+  t_int i, j, k;
+  
+  if(!x->x_w_array_mem_beg)
+    goto NLMSerr_in_tildeperfzero;// this is quick&dirty Musil/Miller style
+  
+  for(i=0; i<n; i++)// store history and convolve
+  {
+    x->x_ref_filt_in_hist[rw_index] = ref_filt_in[i]; // inputs of ref_filt save to history
+    x->x_ref_adapt_in_hist[rw_index] = ref_adapt_in[i]; // inputs of ref_adapt save to history
+    errin = err_in[i];
+    
+		// begin convolution, filter : j++, k--, rw_index = aktueller index fuer lesen schreiben von history und convolution-beginn
+    sum = 0.0f;
+    k = rw_index;
+    for(j=0; j<n_order; j++)
+    {
+      sum += x->x_w_array_mem_beg[j] * x->x_ref_filt_in_hist[k];
+      k--;
+      if(k < 0)
+        k = n_order - 1;
+    }
+    filt_out[i] = sum;
+    
+    
+    if(update)	// downsampling for learn rate
+    {
+      sum = 0.0f;// calculate energy for last n-order samples in filter
+      k = rw_index;
+      for(j=0; j<n_order; j++)	// unrolling quadrature calc
+      {
+        sum += x->x_ref_adapt_in_hist[k] * x->x_ref_adapt_in_hist[k]; // energie
+        k--;
+        if(k < 0)
+          k = n_order - 1;
+      }
+      sum += gammax * gammax * (float)n_order; // convert gammax corresponding to filter order
+      my = beta / sum;// calculate mue
+      
+      my_err = my * errin;
+      
+      k = rw_index;
+      for(j=0; j<n_order; j++) // without unroll
+      {
+        x->x_w_array_mem_beg[j] += x->x_ref_adapt_in_hist[k] * my_err;
+        k--;
+        if(k < 0)
+          k = n_order - 1;
+      }
+    }
+    rw_index++;
+    if(rw_index >= n_order)
+      rw_index = 0;
+  }
+  x->x_rw_index = rw_index; // back to start
+  return(w+7);
+  
+NLMSerr_in_tildeperfzero:
+  
+  while(n--)
+  {
+    *filt_out++ = 0.0f;
+  }
+  return(w+7);
+}
+
+static void NLMSerr_in_tilde_dsp(t_NLMSerr_in_tilde *x, t_signal **sp)
+{
+    x->x_w_array_mem_beg = NLMSerr_in_tilde_check_array(x->x_w_array_sym_name, x->x_n_order);
+
+    if(!x->x_w_array_mem_beg)
+        dsp_add(NLMSerr_in_tilde_perform_zero, 6, sp[0]->s_vec, sp[1]->s_vec, sp[2]->s_vec, sp[3]->s_vec, x, sp[0]->s_n);
+    else
+        dsp_add(NLMSerr_in_tilde_perform, 6, sp[0]->s_vec, sp[1]->s_vec, sp[2]->s_vec, sp[3]->s_vec, x, sp[0]->s_n);
+}
+
+
+/* setup/setdown things */
+
+static void NLMSerr_in_tilde_free(t_NLMSerr_in_tilde *x)
+{
+    freebytes(x->x_ref_filt_in_hist, x->x_n_order*sizeof(t_float));
+    freebytes(x->x_ref_adapt_in_hist, x->x_n_order*sizeof(t_float));
+}
+
+static void *NLMSerr_in_tilde_new(t_symbol *s, t_int argc, t_atom *argv)
+{
+    t_NLMSerr_in_tilde *x = (t_NLMSerr_in_tilde *)pd_new(NLMSerr_in_tilde_class);
+    t_int i, n_order=39;
+    t_symbol    *w_name;
+    t_float beta=0.01f;
+    t_float gammax=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);
+        gammax  = (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(gammax < 0.0f)
+            gammax = 0.0f;
+        if(gammax > 1.0f)
+            gammax = 1.0f;
+        
+        if(n_order < 2)
+            n_order = 2;
+        if(n_order > 1111111)
+            n_order = 1111111;
+        
+        inlet_new(&x->x_obj, &x->x_obj.ob_pd, &s_signal, &s_signal);
+        inlet_new(&x->x_obj, &x->x_obj.ob_pd, &s_signal, &s_signal);
+        outlet_new(&x->x_obj, &s_signal);
+        
+        x->x_flt_sig_in1 = 0;
+        x->x_n_order = n_order;
+        x->x_update = 0;
+        x->x_beta = beta;
+        x->x_gamma = gammax;
+        // 2 times in and one time err_in memory allocation (history)
+        x->x_ref_filt_in_hist = (t_float *)getbytes(x->x_n_order*sizeof(t_float));
+        x->x_ref_adapt_in_hist = (t_float *)getbytes(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_rw_index = 0;
+        
+        return(x);
+    }
+    else
+    {
+        post("NLMSerr_in~-ERROR: need 3 float- + 1 symbol-arguments:");
+        post("  order_of_filter + learnrate_beta + security_value + array_name_taps");
+        return(0);
+    }
+}
+
+void NLMSerr_in_tilde_setup(void)
+{
+    NLMSerr_in_tilde_class = class_new(gensym("NLMSerr_in~"), (t_newmethod)NLMSerr_in_tilde_new, (t_method)NLMSerr_in_tilde_free,
+        sizeof(t_NLMSerr_in_tilde), 0, A_GIMME, 0);
+    CLASS_MAINSIGNALIN(NLMSerr_in_tilde_class, t_NLMSerr_in_tilde, x_flt_sig_in1);
+    class_addmethod(NLMSerr_in_tilde_class, (t_method)NLMSerr_in_tilde_dsp, gensym("dsp"), 0);
+    class_addmethod(NLMSerr_in_tilde_class, (t_method)NLMSerr_in_tilde_update, gensym("update"), A_FLOAT, 0); // method: downsampling factor of learning (multiple of 2^N)
+    class_addmethod(NLMSerr_in_tilde_class, (t_method)NLMSerr_in_tilde_beta, gensym("beta"), A_FLOAT, 0); //method: normalized learning rate
+    class_addmethod(NLMSerr_in_tilde_class, (t_method)NLMSerr_in_tilde_gamma, gensym("gamma"), A_FLOAT, 0);   // method: dithering noise related to signal
+}

externals/iem/iem_adaptfilt/src/NLMS~.c

@@ -6,11 +6,6 @@ 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"
@@ -149,7 +144,7 @@ static t_int *NLMS_tilde_perform(t_int *w)
     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_float gammax = x->x_gamma;
     t_int i, j, update_counter;
     t_int update = x->x_update;
     t_int ord8=n_order&0xfffffff8;
@@ -211,7 +206,7 @@ static t_int *NLMS_tilde_perform(t_int *w)
                 }
                 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
+                sum += gammax * gammax * (float)n_order; // convert gammax corresponding to filter order
                 my = beta / sum;// calculate mue
                 
                 
@@ -270,7 +265,7 @@ static void *NLMS_tilde_new(t_symbol *s, t_int argc, t_atom *argv)
     t_int i, n_order=39;
     t_symbol