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