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Diffstat (limited to 'src/sigNLMS.c')
| -rw-r--r-- | src/sigNLMS.c | 337 |
1 files changed, 0 insertions, 337 deletions
diff --git a/src/sigNLMS.c b/src/sigNLMS.c deleted file mode 100644 index 53ab284..0000000 --- a/src/sigNLMS.c +++ /dev/null @@ -1,337 +0,0 @@ -/* 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 sigNLMS -{ - 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_sigNLMS; - -t_class *sigNLMS_class; - -static t_float *sigNLMS_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 sigNLMS_beta(t_sigNLMS *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 sigNLMS_gamma(t_sigNLMS *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 sigNLMS_update(t_sigNLMS *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 *sigNLMS_perform_zero(t_int *w) -{ - t_sigNLMS *x = (t_sigNLMS *)(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 *sigNLMS_perform(t_int *w) -{ - t_sigNLMS *x = (t_sigNLMS *)(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 sigNLMSperfzero;// 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); - -sigNLMSperfzero: - - while(n--) - { - *filt_out++ = 0.0f; - *err_out++ = 0.0f; - } - return(w+3); -} - -static void sigNLMS_dsp(t_sigNLMS *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 = sigNLMS_check_array(x->x_w_array_sym_name, x->x_n_order); - - if(!x->x_w_array_mem_beg) - dsp_add(sigNLMS_perform_zero, 2, x, n); - else - dsp_add(sigNLMS_perform, 2, x, n); -} - - -/* setup/setdown things */ - -static void sigNLMS_free(t_sigNLMS *x) -{ - freebytes(x->x_in_hist, 2*x->x_n_order*sizeof(t_float)); -} - -static void *sigNLMS_new(t_symbol *s, t_int argc, t_atom *argv) -{ - t_sigNLMS *x = (t_sigNLMS *)pd_new(sigNLMS_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 sigNLMS_setup(void) -{ - sigNLMS_class = class_new(gensym("NLMS~"), (t_newmethod)sigNLMS_new, (t_method)sigNLMS_free, - sizeof(t_sigNLMS), 0, A_GIMME, 0); - CLASS_MAINSIGNALIN(sigNLMS_class, t_sigNLMS, x_msi); - class_addmethod(sigNLMS_class, (t_method)sigNLMS_dsp, gensym("dsp"), 0); - class_addmethod(sigNLMS_class, (t_method)sigNLMS_update, gensym("update"), A_FLOAT, 0); // method: downsampling factor of learning (multiple of 2^N) - class_addmethod(sigNLMS_class, (t_method)sigNLMS_beta, gensym("beta"), A_FLOAT, 0); //method: normalized learning rate - class_addmethod(sigNLMS_class, (t_method)sigNLMS_gamma, gensym("gamma"), A_FLOAT, 0); // method: dithering noise related to signal - class_sethelpsymbol(sigNLMS_class, gensym("iemhelp2/NLMS~")); -} |