From 1d7b807215430e7edbe04213885c345a420134b3 Mon Sep 17 00:00:00 2001
From: Georg Holzmann <grholzi@users.sourceforge.net>
Date: Thu, 11 Jan 2007 17:38:56 +0000
Subject: removed the files with deprecated naming convention

svn path=/trunk/externals/iem/iem_adaptfilt/; revision=7292
---
 src/sign_CNLMS.c | 482 -------------------------------------------------------
 1 file changed, 482 deletions(-)
 delete mode 100644 src/sign_CNLMS.c

(limited to 'src/sign_CNLMS.c')

diff --git a/src/sign_CNLMS.c b/src/sign_CNLMS.c
deleted file mode 100644
index a97c1ed..0000000
--- a/src/sign_CNLMS.c
+++ /dev/null
@@ -1,482 +0,0 @@
-/* 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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