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Diffstat (limited to 'src/sign_CNLMS.c')
| -rw-r--r-- | src/sign_CNLMS.c | 482 |
1 files changed, 0 insertions, 482 deletions
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~")); -} |