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Diffstat (limited to 'src')
| -rw-r--r-- | src/NLMSerr_in~.c | 331 |
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 +} |