X-Git-Url: https://git.sesse.net/?p=c64tapwav;a=blobdiff_plain;f=decode.cpp;h=ab242d0be5845f7145e0b40ca75c5a646207ca25;hp=43d6766bfd38774c58f4b6fc4601f7c18f598bb0;hb=b0572bf7f70621037e6692c392965f2e85ea2590;hpb=7845bf4cd343d68c3314c7f30a10eada1bd714a7 diff --git a/decode.cpp b/decode.cpp index 43d6766..ab242d0 100644 --- a/decode.cpp +++ b/decode.cpp @@ -9,6 +9,7 @@ #include "audioreader.h" #include "interpolate.h" +#include "level.h" #include "tap.h" #define BUFSIZE 4096 @@ -18,7 +19,14 @@ #define SYNC_PULSE_LENGTH 378.0 #define SYNC_TEST_TOLERANCE 1.10 +// SPSA options #define NUM_FILTER_COEFF 32 +#define NUM_ITER 5000 +#define A NUM_ITER/10 // approx +#define INITIAL_A 0.005 // A bit of trial and error... +#define INITIAL_C 0.02 // This too. +#define GAMMA 0.166 +#define ALPHA 1.0 static float hysteresis_limit = 3000.0 / 32768.0; static bool do_calibrate = true; @@ -29,6 +37,10 @@ static float crop_start = 0.0f, crop_end = HUGE_VAL; static float filter_coeff[NUM_FILTER_COEFF] = { 1.0f }; // The rest is filled with 0. static bool output_filtered = false; static bool quiet = false; +static bool do_auto_level = false; +static bool output_leveled = false; +static std::vector train_snap_points; +static bool do_train = false; // between [x,x+1] double find_zerocrossing(const std::vector &pcm, int x) @@ -151,6 +163,7 @@ void output_tap(const std::vector& pulses, double calibration_factor) } static struct option long_options[] = { + {"auto-level", 0, 0, 'a' }, {"no-calibrate", 0, 0, 's' }, {"plot-cycles", 0, 0, 'p' }, {"hysteresis-limit", required_argument, 0, 'l' }, @@ -166,12 +179,16 @@ void help() { fprintf(stderr, "decode [OPTIONS] AUDIO-FILE > TAP-FILE\n"); 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, " -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"); fprintf(stderr, " -f, --filter C1:C2:C3:... specify FIR filter (up to %d coefficients)\n", NUM_FILTER_COEFF); 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"); + fprintf(stderr, " (implies --no-calibrate and --quiet unless overridden)\n"); fprintf(stderr, " -q, --quiet suppress some informational messages\n"); fprintf(stderr, " -h, --help display this help, then exit\n"); exit(1); @@ -181,11 +198,19 @@ void parse_options(int argc, char **argv) { for ( ;; ) { int option_index = 0; - int c = getopt_long(argc, argv, "spl:f:Fc:qh", long_options, &option_index); + int c = getopt_long(argc, argv, "aAspl:f:Fc:t:qh", long_options, &option_index); if (c == -1) break; switch (c) { + case 'a': + do_auto_level = true; + break; + + case 'A': + output_leveled = true; + break; + case 's': do_calibrate = false; break; @@ -199,11 +224,11 @@ void parse_options(int argc, char **argv) break; case 'f': { - const char *coeffstr = strtok(optarg, ":"); + const char *coeffstr = strtok(optarg, ": "); int coeff_index = 0; while (coeff_index < NUM_FILTER_COEFF && coeffstr != NULL) { filter_coeff[coeff_index++] = atof(coeffstr); - coeffstr = strtok(NULL, ":"); + coeffstr = strtok(NULL, ": "); } use_filter = true; break; @@ -226,6 +251,20 @@ void parse_options(int argc, char **argv) break; } + case 't': { + const char *cyclestr = strtok(optarg, ":"); + while (cyclestr != NULL) { + train_snap_points.push_back(atof(cyclestr)); + cyclestr = strtok(NULL, ":"); + } + do_train = true; + + // Set reasonable defaults (can be overridden later on the command line). + do_calibrate = false; + quiet = true; + break; + } + case 'q': quiet = true; break; @@ -319,6 +358,86 @@ std::vector detect_pulses(const std::vector &pcm, int sample_rate) return pulses; } +void output_cycle_plot(const std::vector &pulses, double calibration_factor) +{ + FILE *fp = fopen("cycles.plot", "w"); + for (unsigned i = 0; i < pulses.size(); ++i) { + double cycles = pulses[i].len * calibration_factor * C64_FREQUENCY; + fprintf(fp, "%f %f\n", pulses[i].time, cycles); + } + fclose(fp); +} + +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; + } + return sqrt(sum_badness / (pulses.size() - 1)); +} + +void spsa_train(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; + double best_badness = HUGE_VAL; + + for (int n = 1; n < NUM_ITER; ++n) { + float a = INITIAL_A * pow(n + A, -ALPHA); + float c = start_c * pow(n, -GAMMA); + + // find a random perturbation + float p[NUM_FILTER_COEFF]; + float filter1[NUM_FILTER_COEFF], filter2[NUM_FILTER_COEFF]; + for (int i = 0; i < NUM_FILTER_COEFF; ++i) { + p[i] = (rand() % 2) ? 1.0 : -1.0; + filter1[i] = std::max(std::min(filter[i] - c * p[i], 1.0f), -1.0f); + 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); + float badness1 = eval_badness(pulses1, 1.0); + float badness2 = eval_badness(pulses2, 1.0); + + // Find the gradient estimator + float g[NUM_FILTER_COEFF]; + for (int i = 0; i < NUM_FILTER_COEFF; ++i) { + g[i] = (badness2 - badness1) / (2.0 * c * p[i]); + filter[i] -= a * g[i]; + filter[i] = std::max(std::min(filter[i], 1.0f), -1.0f); + } + if (badness2 < badness1) { + std::swap(badness1, badness2); + std::swap(filter1, filter2); + std::swap(pulses1, pulses2); + } + if (badness1 < best_badness) { + printf("\nNew best filter (badness=%f):", badness1); + for (int i = 0; i < NUM_FILTER_COEFF; ++i) { + printf(" %.5f", filter1[i]); + } + best_badness = badness1; + printf("\n"); + + if (output_cycles_plot) { + output_cycle_plot(pulses1, 1.0); + } + } + printf("%d ", n); + fflush(stdout); + } +} + int main(int argc, char **argv) { parse_options(argc, argv); @@ -338,6 +457,15 @@ int main(int argc, char **argv) pcm = do_filter(pcm, filter_coeff); } + if (do_auto_level) { + pcm = level_samples(pcm, sample_rate); + if (output_leveled) { + FILE *fp = fopen("leveled.raw", "wb"); + fwrite(pcm.data(), pcm.size() * sizeof(pcm[0]), 1, fp); + fclose(fp); + } + } + #if 0 for (int i = 0; i < LEN; ++i) { in[i] += rand() % 10000; @@ -350,6 +478,11 @@ int main(int argc, char **argv) } #endif + if (do_train) { + spsa_train(pcm, sample_rate); + exit(0); + } + std::vector pulses = detect_pulses(pcm, sample_rate); double calibration_factor = 1.0; @@ -358,12 +491,7 @@ int main(int argc, char **argv) } if (output_cycles_plot) { - FILE *fp = fopen("cycles.plot", "w"); - for (unsigned i = 0; i < pulses.size(); ++i) { - double cycles = pulses[i].len * calibration_factor * C64_FREQUENCY; - fprintf(fp, "%f %f\n", pulses[i].time, cycles); - } - fclose(fp); + output_cycle_plot(pulses, calibration_factor); } output_tap(pulses, calibration_factor);