X-Git-Url: https://git.sesse.net/?p=c64tapwav;a=blobdiff_plain;f=decode.cpp;h=7d5fe4338f5f38586d9ec428d273d3fb34fa2115;hp=ab242d0be5845f7145e0b40ca75c5a646207ca25;hb=1d431da4497326b4496cdc6596c52736d98699e7;hpb=b0572bf7f70621037e6692c392965f2e85ea2590 diff --git a/decode.cpp b/decode.cpp index ab242d0..7d5fe43 100644 --- a/decode.cpp +++ b/decode.cpp @@ -42,6 +42,12 @@ static bool output_leveled = false; static std::vector train_snap_points; static bool do_train = false; +// The minimum estimated sound level (for do_auto_level) at any given point. +// If you decrease this, you'll be able to amplify really silent signals +// by more, but you'll also increase the level of silent (ie. noise-only) segments, +// possibly caused misdetected pulses in these segments. +static float min_level = 0.05f; + // between [x,x+1] double find_zerocrossing(const std::vector &pcm, int x) { @@ -164,6 +170,7 @@ void output_tap(const std::vector& pulses, double calibration_factor) static struct option long_options[] = { {"auto-level", 0, 0, 'a' }, + {"output-leveled", 0, 0, 'A' }, {"no-calibrate", 0, 0, 's' }, {"plot-cycles", 0, 0, 'p' }, {"hysteresis-limit", required_argument, 0, 'l' }, @@ -181,6 +188,7 @@ void help() fprintf(stderr, "\n"); fprintf(stderr, " -a, --auto-level automatically adjust amplitude levels throughout the file\n"); fprintf(stderr, " -A, --output-leveled output leveled waveform to leveled.raw\n"); + fprintf(stderr, " -m, --min-level minimum estimated sound level (0..32768) for --auto-level\n"); fprintf(stderr, " -s, --no-calibrate do not try to calibrate on sync pulse length\n"); 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"); @@ -198,7 +206,7 @@ void parse_options(int argc, char **argv) { for ( ;; ) { int option_index = 0; - int c = getopt_long(argc, argv, "aAspl:f:Fc:t:qh", long_options, &option_index); + int c = getopt_long(argc, argv, "aAm:spl:f:Fc:t:qh", long_options, &option_index); if (c == -1) break; @@ -211,6 +219,10 @@ void parse_options(int argc, char **argv) output_leveled = true; break; + case 'm': + min_level = atof(optarg) / 32768.0; + break; + case 's': do_calibrate = false; break; @@ -368,24 +380,80 @@ 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) { - // Train! float filter[NUM_FILTER_COEFF] = { 1.0f }; // The rest is filled with 0. float start_c = INITIAL_C; @@ -429,6 +497,8 @@ 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); } @@ -458,7 +528,7 @@ int main(int argc, char **argv) } if (do_auto_level) { - pcm = level_samples(pcm, sample_rate); + pcm = level_samples(pcm, min_level, sample_rate); if (output_leveled) { FILE *fp = fopen("leveled.raw", "wb"); fwrite(pcm.data(), pcm.size() * sizeof(pcm[0]), 1, fp);