diff options
Diffstat (limited to 'src')
| -rw-r--r-- | src/NLMSerr_in~.c | 291 |
1 files changed, 132 insertions, 159 deletions
diff --git a/src/NLMSerr_in~.c b/src/NLMSerr_in~.c index 8c0c6dd..f48f302 100644 --- a/src/NLMSerr_in~.c +++ b/src/NLMSerr_in~.c @@ -28,203 +28,171 @@ noisternig_AT_iem.at; musil_AT_iem.at typedef struct NLMSerr_in_tilde { - t_object x_obj; + t_object x_obj;// common pd object structure t_symbol *x_w_array_sym_name; t_float *x_w_array_mem_beg; - 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_ref_filt_in_hist;// history buffer for input signal reference for internal convolution = filter + t_float *x_ref_adapt_in_hist;// history buffer for input signal reference for internal adapting filter + t_int x_rw_index;// current read-write-index in circular buffer + t_int x_n_order;// order of filter or convolution + t_int x_update;// binary update parameter ON / OFF t_float x_beta;// learn rate [0 .. 2] - t_float x_gamma;// regularization - t_float x_sig_in2; + t_float x_gamma;// regularization Parameter = minimum + t_float x_flt_sig_in1; } 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); - } + 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; + 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; + 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; + t_int i=1, u = (t_int)f; + + if(u != 0) + u = 1; + 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[4]); - t_int n = (t_int)(w[5]); - t_float *filt_out = (t_float *)(w[3]); + t_NLMSerr_in_tilde *x = (t_NLMSerr_in_tilde *)(w[5]); + t_int n = (t_int)(w[6]); + t_float *filt_out = (t_float *)(w[4]); t_int i; for(i=0; i<n; i++) { *filt_out++ = 0.0f; } - return (w+6); + return (w+7); } static t_int *NLMSerr_in_tilde_perform(t_int *w) { - t_NLMSerr_in_tilde *x = (t_NLMSerr_in_tilde *)(w[4]); - t_int n = (t_int)(w[5]); - t_int n_order = x->x_n_order; /* number of filter-order */ - t_int rw_index = x->x_rw_index; - t_float *filt_in = (t_float *)(w[1]);// first sig in - t_float *err_in = (t_float *)(w[2]);// second sig in - t_float *filt_out = (t_float *)(w[3]);// 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, errin; - t_float beta = x->x_beta; - t_float gammax = 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 + t_int n = (t_int)(w[6]); + t_NLMSerr_in_tilde *x = (t_NLMSerr_in_tilde *)(w[5]); + t_float *filt_out = (t_float *)(w[4]);// first sig out + t_float *err_in = (t_float *)(w[3]);// third sig in + t_float *ref_adapt_in = (t_float *)(w[2]);// second sig in + t_float *ref_filt_in = (t_float *)(w[1]);// first sig in + t_int n_order = x->x_n_order; /* number of filter-order */ + t_int rw_index = x->x_rw_index; /* current read write index in circular buffer */ + t_int update = x->x_update; + t_float beta = x->x_beta; /* learn rate */ + t_float gammax = x->x_gamma; /* minimum energy */ + t_float my, my_err, sum, errin; + t_int i, j, k; + + if(!x->x_w_array_mem_beg) + goto NLMSerr_in_tildeperfzero;// this is quick&dirty Musil/Miller style + + for(i=0; i<n; i++)// store history and convolve + { + x->x_ref_filt_in_hist[rw_index] = ref_filt_in[i]; // inputs of ref_filt save to history + x->x_ref_adapt_in_hist[rw_index] = ref_adapt_in[i]; // inputs of ref_adapt save to history + errin = err_in[i]; - for(i=0, update_counter=0; i<n; i++)// store history and convolve + // begin convolution, filter : j++, k--, rw_index = aktueller index fuer lesen schreiben von history und convolution-beginn + sum = 0.0f; + k = rw_index; + for(j=0; j<n_order; j++) { - write_in_hist1[rw_index] = filt_in[i]; // save inputs to variable & history - write_in_hist2[rw_index] = filt_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 += gammax * gammax * (float)n_order; // convert gammax 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; + sum += x->x_w_array_mem_beg[j] * x->x_ref_filt_in_hist[k]; + k--; + if(k < 0) + k = n_order - 1; } - - x->x_rw_index = rw_index; // back to start - - return(w+6); + filt_out[i] = sum; -NLMSerr_in_tildeperfzero: - while(n--) + if(update) // downsampling for learn rate { - *filt_out++ = 0.0f; + sum = 0.0f;// calculate energy for last n-order samples in filter + k = rw_index; + for(j=0; j<n_order; j++) // unrolling quadrature calc + { + sum += x->x_ref_adapt_in_hist[k] * x->x_ref_adapt_in_hist[k]; // energie + k--; + if(k < 0) + k = n_order - 1; + } + sum += gammax * gammax * (float)n_order; // convert gammax corresponding to filter order + my = beta / sum;// calculate mue + + my_err = my * errin; + + k = rw_index; + for(j=0; j<n_order; j++) // without unroll + { + x->x_w_array_mem_beg[j] += x->x_ref_adapt_in_hist[k] * my_err; + k--; + if(k < 0) + k = n_order - 1; + } } - return(w+6); + rw_index++; + if(rw_index >= n_order) + rw_index = 0; + } + x->x_rw_index = rw_index; // back to start + return(w+7); + +NLMSerr_in_tildeperfzero: + + while(n--) + { + *filt_out++ = 0.0f; + } + return(w+7); } static void NLMSerr_in_tilde_dsp(t_NLMSerr_in_tilde *x, t_signal **sp) @@ -232,9 +200,9 @@ static void NLMSerr_in_tilde_dsp(t_NLMSerr_in_tilde *x, t_signal **sp) 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, 5, sp[0]->s_vec, sp[1]->s_vec, sp[2]->s_vec, x, sp[0]->s_n); + dsp_add(NLMSerr_in_tilde_perform_zero, 6, sp[0]->s_vec, sp[1]->s_vec, sp[2]->s_vec, sp[3]->s_vec, x, sp[0]->s_n); else - dsp_add(NLMSerr_in_tilde_perform, 5, sp[0]->s_vec, sp[1]->s_vec, sp[2]->s_vec, x, sp[0]->s_n); + dsp_add(NLMSerr_in_tilde_perform, 6, sp[0]->s_vec, sp[1]->s_vec, sp[2]->s_vec, sp[3]->s_vec, x, sp[0]->s_n); } @@ -242,7 +210,8 @@ static void NLMSerr_in_tilde_dsp(t_NLMSerr_in_tilde *x, t_signal **sp) static void NLMSerr_in_tilde_free(t_NLMSerr_in_tilde *x) { - freebytes(x->x_in_hist, 2*x->x_n_order*sizeof(t_float)); + freebytes(x->x_ref_filt_in_hist, x->x_n_order*sizeof(t_float)); + freebytes(x->x_ref_adapt_in_hist, x->x_n_order*sizeof(t_float)); } static void *NLMSerr_in_tilde_new(t_symbol *s, t_int argc, t_atom *argv) @@ -250,7 +219,7 @@ 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 beta=0.01f; t_float gammax=0.00001f; if((argc >= 4) && @@ -280,20 +249,24 @@ static void *NLMSerr_in_tilde_new(t_symbol *s, t_int argc, t_atom *argv) n_order = 1111111; inlet_new(&x->x_obj, &x->x_obj.ob_pd, &s_signal, &s_signal); + inlet_new(&x->x_obj, &x->x_obj.ob_pd, &s_signal, &s_signal); outlet_new(&x->x_obj, &s_signal); - x->x_sig_in2 = 0; + x->x_flt_sig_in1 = 0; x->x_n_order = n_order; x->x_update = 0; x->x_beta = beta; x->x_gamma = gammax; // 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)); + x->x_ref_filt_in_hist = (t_float *)getbytes(x->x_n_order*sizeof(t_float)); + x->x_ref_adapt_in_hist = (t_float *)getbytes(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; + x->x_rw_index = 0; + return(x); } else @@ -308,7 +281,7 @@ 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_sig_in2); + CLASS_MAINSIGNALIN(NLMSerr_in_tilde_class, t_NLMSerr_in_tilde, x_flt_sig_in1); 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 |