initial ci of NLMS object without feedback subtraction

svn path=/trunk/externals/iem/iem_adaptfilt/; revision=14738

1 files changed, 331 insertions, 0 deletions

diff --git a/src/NLMSerr_in~.c b/src/NLMSerr_in~.c
new file mode 100644
index 0000000..a94e6ad
--- /dev/null
+++ b/src/NLMSerr_in~.c
@@ -0,0 +1,331 @@
+/* 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>
+
+
+/* ----------------------- 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;
+    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_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 = 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 *NLMSerr_in_tilde_perform_zero(t_int *w)
+{
+  t_NLMSerr_in_tilde *x = (t_NLMSerr_in_tilde *)(w[1]);
+  t_int n = (t_int)(w[2]);
+  
+  t_float **io = x->x_io_ptr_beg;
+  t_float *out;
+  t_int i;
+  
+  
+  out = io[2];
+  for(i=0; i<n; i++)
+  {
+    *out++ = 0.0f;
+  }
+  return (w+3);
+}
+
+static t_int *NLMSerr_in_tilde_perform(t_int *w)
+{
+    t_NLMSerr_in_tilde *x = (t_NLMSerr_in_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 *err_in = x->x_io_ptr_beg[1], errin;// second sig in
+    t_float *filt_out = x->x_io_ptr_beg[2];// first 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 NLMSerr_in_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];
+        errin = err_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;
+        
+        
+        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 * errin;
+                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);
+    
+NLMSerr_in_tildeperfzero:
+    
+    while(n--)
+    {
+        *filt_out++ = 0.0f;
+    }
+    return(w+3);
+}
+
+static void NLMSerr_in_tilde_dsp(t_NLMSerr_in_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 = 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, 2, x, n);
+    else
+        dsp_add(NLMSerr_in_tilde_perform, 2, x, n);
+}
+
+
+/* setup/setdown things */
+
+static void NLMSerr_in_tilde_free(t_NLMSerr_in_tilde *x)
+{
+    freebytes(x->x_in_hist, 2*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=0.1f;
+    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);
+        
+        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 err_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("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_msi);
+    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
+}