From d056668887fde2dd2a784ef3507ec35e98131439 Mon Sep 17 00:00:00 2001
From: "N.N." <mnoi@users.sourceforge.net>
Date: Wed, 2 Aug 2006 14:02:28 +0000
Subject: no message

svn path=/trunk/externals/iem/iem_adaptfilt/; revision=5455
---
 src/sign_CNLMS.c | 482 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 482 insertions(+)
 create mode 100644 src/sign_CNLMS.c

(limited to 'src/sign_CNLMS.c')

diff --git a/src/sign_CNLMS.c b/src/sign_CNLMS.c
new file mode 100644
index 0000000..a97c1ed
--- /dev/null
+++ b/src/sign_CNLMS.c
@@ -0,0 +1,482 @@
+/* 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~"));
+}
-- 
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