X-Git-Url: https://git.sesse.net/?p=c64tapwav;a=blobdiff_plain;f=decode.cpp;h=ef8630de9948532f66e324d82a35485f74e24aa9;hp=930b1933d52a343ba45f61dc60b1c8bcd9bd1a4a;hb=6d341e3e890484f23ce2b4ab057f344f13e32811;hpb=940c35c53ea0355e1c08c446ae33fda54df744c6 diff --git a/decode.cpp b/decode.cpp index 930b193..ef8630d 100644 --- a/decode.cpp +++ b/decode.cpp @@ -1,3 +1,6 @@ +// Copyright Steinar H. Gunderson +// Licensed under the GPL, v2. (See the file COPYING.) + #include #include #include @@ -11,6 +14,7 @@ #include "interpolate.h" #include "level.h" #include "tap.h" +#include "filter.h" #define BUFSIZE 4096 #define C64_FREQUENCY 985248 @@ -31,11 +35,15 @@ static float hysteresis_limit = 3000.0 / 32768.0; static bool do_calibrate = true; static bool output_cycles_plot = false; -static bool use_filter = false; static bool do_crop = false; static float crop_start = 0.0f, crop_end = HUGE_VAL; + +static bool use_fir_filter = false; static float filter_coeff[NUM_FILTER_COEFF] = { 1.0f }; // The rest is filled with 0. +static bool use_rc_filter = false; +static float rc_filter_freq; static bool output_filtered = false; + static bool quiet = false; static bool do_auto_level = false; static bool output_leveled = false; @@ -175,6 +183,7 @@ static struct option long_options[] = { {"plot-cycles", 0, 0, 'p' }, {"hysteresis-limit", required_argument, 0, 'l' }, {"filter", required_argument, 0, 'f' }, + {"rc-filter", required_argument, 0, 'r' }, {"output-filtered", 0, 0, 'F' }, {"crop", required_argument, 0, 'c' }, {"quiet", 0, 0, 'q' }, @@ -193,6 +202,7 @@ void help() fprintf(stderr, " -p, --plot-cycles output debugging info to cycles.plot\n"); fprintf(stderr, " -l, --hysteresis-limit VAL change amplitude threshold for ignoring pulses (0..32768)\n"); fprintf(stderr, " -f, --filter C1:C2:C3:... specify FIR filter (up to %d coefficients)\n", NUM_FILTER_COEFF); + fprintf(stderr, " -r, --rc-filter FREQ send signal through a highpass RC filter with given frequency (in Hertz)\n"); fprintf(stderr, " -F, --output-filtered output filtered waveform to filtered.raw\n"); fprintf(stderr, " -c, --crop START[:END] use only the given part of the file\n"); fprintf(stderr, " -t, --train LEN1:LEN2:... train a filter for detecting any of the given number of cycles\n"); @@ -206,7 +216,7 @@ void parse_options(int argc, char **argv) { for ( ;; ) { int option_index = 0; - int c = getopt_long(argc, argv, "aAm:spl:f:Fc:t:qh", long_options, &option_index); + int c = getopt_long(argc, argv, "aAm:spl:f:r:Fc:t:qh", long_options, &option_index); if (c == -1) break; @@ -242,10 +252,15 @@ void parse_options(int argc, char **argv) filter_coeff[coeff_index++] = atof(coeffstr); coeffstr = strtok(NULL, ": "); } - use_filter = true; + use_fir_filter = true; break; } + case 'r': + use_rc_filter = true; + rc_filter_freq = atof(optarg); + break; + case 'F': output_filtered = true; break; @@ -302,7 +317,7 @@ std::vector crop(const std::vector& pcm, float crop_start, float c } // TODO: Support AVX here. -std::vector do_filter(const std::vector& pcm, const float* filter) +std::vector do_fir_filter(const std::vector& pcm, const float* filter) { std::vector filtered_pcm; filtered_pcm.reserve(pcm.size()); @@ -323,6 +338,24 @@ std::vector do_filter(const std::vector& pcm, const float* filter) return filtered_pcm; } +std::vector do_rc_filter(const std::vector& pcm, float freq, int sample_rate) +{ + std::vector filtered_pcm; + filtered_pcm.resize(pcm.size()); + Filter filter = Filter::hpf(2.0 * M_PI * freq / sample_rate); + for (unsigned i = 0; i < pcm.size(); ++i) { + filtered_pcm[i] = filter.update(pcm[i]); + } + + if (output_filtered) { + FILE *fp = fopen("filtered.raw", "wb"); + fwrite(filtered_pcm.data(), filtered_pcm.size() * sizeof(filtered_pcm[0]), 1, fp); + fclose(fp); + } + + return filtered_pcm; +} + std::vector detect_pulses(const std::vector &pcm, int sample_rate) { std::vector pulses; @@ -380,22 +413,79 @@ void output_cycle_plot(const std::vector &pulses, double calibration_fact fclose(fp); } +std::pair find_closest_point(double x, const std::vector &points) +{ + int best_point = 0; + double best_dist = (x - points[0]) * (x - points[0]); + for (unsigned j = 1; j < train_snap_points.size(); ++j) { + double dist = (x - points[j]) * (x - points[j]); + if (dist < best_dist) { + best_point = j; + best_dist = dist; + } + } + return std::make_pair(best_point, best_dist); +} + float eval_badness(const std::vector& pulses, double calibration_factor) { double sum_badness = 0.0; for (unsigned i = 0; i < pulses.size(); ++i) { double cycles = pulses[i].len * calibration_factor * C64_FREQUENCY; if (cycles > 2000.0) cycles = 2000.0; // Don't make pauses arbitrarily bad. - double badness = (cycles - train_snap_points[0]) * (cycles - train_snap_points[0]); - for (unsigned j = 1; j < train_snap_points.size(); ++j) { - badness = std::min(badness, (cycles - train_snap_points[j]) * (cycles - train_snap_points[j])); - } - sum_badness += badness; + std::pair selected_point_and_sq_dist = find_closest_point(cycles, train_snap_points); + sum_badness += selected_point_and_sq_dist.second; } return sqrt(sum_badness / (pulses.size() - 1)); } -void spsa_train(std::vector &pcm, int sample_rate) +void find_kmeans(const std::vector &pulses, double calibration_factor, const std::vector &initial_centers) +{ + std::vector last_centers = initial_centers; + std::vector sums; + std::vector num; + sums.resize(initial_centers.size()); + num.resize(initial_centers.size()); + for ( ;; ) { + for (unsigned i = 0; i < initial_centers.size(); ++i) { + sums[i] = 0.0f; + num[i] = 0; + } + for (unsigned i = 0; i < pulses.size(); ++i) { + double cycles = pulses[i].len * calibration_factor * C64_FREQUENCY; + // Ignore heavy outliers, which are almost always long pauses. + if (cycles > 2000.0) { + continue; + } + std::pair selected_point_and_sq_dist = find_closest_point(cycles, last_centers); + int p = selected_point_and_sq_dist.first; + sums[p] += cycles; + ++num[p]; + } + bool any_moved = false; + for (unsigned i = 0; i < initial_centers.size(); ++i) { + if (num[i] == 0) { + printf("K-means broke down, can't output new reference training points\n"); + return; + } + float new_center = sums[i] / num[i]; + if (fabs(new_center - last_centers[i]) > 1e-3) { + any_moved = true; + } + last_centers[i] = new_center; + } + if (!any_moved) { + break; + } + } + printf("New reference training points:"); + for (unsigned i = 0; i < last_centers.size(); ++i) { + printf(" %.3f", last_centers[i]); + } + printf("\n"); +} + +void spsa_train(const std::vector &pcm, int sample_rate) { float filter[NUM_FILTER_COEFF] = { 1.0f }; // The rest is filled with 0. @@ -415,8 +505,8 @@ void spsa_train(std::vector &pcm, int sample_rate) filter2[i] = std::max(std::min(filter[i] + c * p[i], 1.0f), -1.0f); } - std::vector pulses1 = detect_pulses(do_filter(pcm, filter1), sample_rate); - std::vector pulses2 = detect_pulses(do_filter(pcm, filter2), sample_rate); + std::vector pulses1 = detect_pulses(do_fir_filter(pcm, filter1), sample_rate); + std::vector pulses2 = detect_pulses(do_fir_filter(pcm, filter2), sample_rate); float badness1 = eval_badness(pulses1, 1.0); float badness2 = eval_badness(pulses2, 1.0); @@ -440,6 +530,8 @@ void spsa_train(std::vector &pcm, int sample_rate) best_badness = badness1; printf("\n"); + find_kmeans(pulses1, 1.0, train_snap_points); + if (output_cycles_plot) { output_cycle_plot(pulses1, 1.0); } @@ -464,8 +556,12 @@ int main(int argc, char **argv) pcm = crop(pcm, crop_start, crop_end, sample_rate); } - if (use_filter) { - pcm = do_filter(pcm, filter_coeff); + if (use_fir_filter) { + pcm = do_fir_filter(pcm, filter_coeff); + } + + if (use_rc_filter) { + pcm = do_rc_filter(pcm, rc_filter_freq, sample_rate); } if (do_auto_level) {