git.sesse.net Git - c64tapwav/blob - decode.cpp

   1 // Copyright Steinar H. Gunderson <sgunderson@bigfoot.com>
   2 // Licensed under the GPL, v2. (See the file COPYING.)
   3
   4 #include <stdio.h>
   5 #include <string.h>
   6 #include <math.h>
   7 #include <assert.h>
   8 #include <limits.h>
   9 #include <getopt.h>
  10 #ifdef __AVX__
  11 #include <immintrin.h>
  12 #endif
  13 #include <vector>
  14 #include <algorithm>
  15
  16 #include "audioreader.h"
  17 #include "interpolate.h"
  18 #include "level.h"
  19 #include "tap.h"
  20 #include "filter.h"
  21
  22 #define BUFSIZE 4096
  23 #define C64_FREQUENCY 985248
  24 #define SYNC_PULSE_START 1000
  25 #define SYNC_PULSE_END 20000
  26 #define SYNC_PULSE_LENGTH 378.0
  27 #define SYNC_TEST_TOLERANCE 1.10
  28
  29 // SPSA options
  30 #define NUM_FILTER_COEFF 32
  31 #define NUM_SPSA_VALS (NUM_FILTER_COEFF + 2)
  32 #define NUM_ITER 5000
  33 #define A NUM_ITER/10  // approx
  34 #define INITIAL_A 0.005 // A bit of trial and error...
  35 #define INITIAL_C 0.02  // This too.
  36 #define GAMMA 0.166
  37 #define ALPHA 1.0
  38
  39 static float hysteresis_upper_limit = 0.1;
  40 static float hysteresis_lower_limit = -0.1;
  41 static bool do_calibrate = true;
  42 static bool output_cycles_plot = false;
  43 static bool do_crop = false;
  44 static float crop_start = 0.0f, crop_end = HUGE_VAL;
  45
  46 static bool use_fir_filter = false;
  47 static float filter_coeff[NUM_FILTER_COEFF] = { 1.0f };  // The rest is filled with 0.
  48 static bool use_rc_filter = false;
  49 static float rc_filter_freq;
  50 static bool output_filtered = false;
  51
  52 static bool quiet = false;
  53 static bool do_auto_level = false;
  54 static bool output_leveled = false;
  55 static std::vector<float> train_snap_points;
  56 static bool do_train = false;
  57
  58 // The frequency to filter on (for do_auto_level), in Hertz.
  59 // Larger values makes the compressor react faster, but if it is too large,
  60 // you'll ruin the waveforms themselves.
  61 static float auto_level_freq = 200.0;
  62
  63 // The minimum estimated sound level (for do_auto_level) at any given point.
  64 // If you decrease this, you'll be able to amplify really silent signals
  65 // by more, but you'll also increase the level of silent (ie. noise-only) segments,
  66 // possibly caused misdetected pulses in these segments.
  67 static float min_level = 0.05f;
  68
  69 // search for the value <limit> between [x,x+1]
  70 template<bool fast>
  71 double find_crossing(const std::vector<float> &pcm, int x, float limit)
  72 {
  73         if (fast) {
  74                 // Do simple linear interpolation.
  75                 return x + (limit - pcm[x]) / (pcm[x + 1] - pcm[x]);
  76         } else {
  77                 // Binary search for the zero crossing as given by Lanczos interpolation.
  78                 double upper = x;
  79                 double lower = x + 1;
  80                 while (lower - upper > 1e-3) {
  81                         double mid = 0.5f * (upper + lower);
  82                         if (lanczos_interpolate(pcm, mid) > limit) {
  83                                 upper = mid;
  84                         } else {
  85                                 lower = mid;
  86                         }
  87                 }
  88
  89                 return 0.5f * (upper + lower);
  90         }
  91 }
  92
  93 struct pulse {
  94         double time;  // in seconds from start
  95         double len;   // in seconds
  96 };
  97
  98 // Calibrate on the first ~25k pulses (skip a few, just to be sure).
  99 double calibrate(const std::vector<pulse> &pulses) {
 100         if (pulses.size() < SYNC_PULSE_END) {
 101                 fprintf(stderr, "Too few pulses, not calibrating!\n");
 102                 return 1.0;
 103         }
 104
 105         int sync_pulse_end = -1;
 106         double sync_pulse_stddev = -1.0;
 107
 108         // Compute the standard deviation (to check for uneven speeds).
 109         // If it suddenly skyrockets, we assume that sync ended earlier
 110         // than we thought (it should be 25000 cycles), and that we should
 111         // calibrate on fewer cycles.
 112         for (int try_end : { 2000, 4000, 5000, 7500, 10000, 15000, SYNC_PULSE_END }) {
 113                 double sum2 = 0.0;
 114                 for (int i = SYNC_PULSE_START; i < try_end; ++i) {
 115                         double cycles = pulses[i].len * C64_FREQUENCY;
 116                         sum2 += (cycles - SYNC_PULSE_LENGTH) * (cycles - SYNC_PULSE_LENGTH);
 117                 }
 118                 double stddev = sqrt(sum2 / (try_end - SYNC_PULSE_START - 1));
 119                 if (sync_pulse_end != -1 && stddev > 5.0 && stddev / sync_pulse_stddev > 1.3) {
 120                         fprintf(stderr, "Stopping at %d sync pulses because standard deviation would be too big (%.2f cycles); shorter-than-usual trailer?\n",
 121                                 sync_pulse_end, stddev);
 122                         break;
 123                 }
 124                 sync_pulse_end = try_end;
 125                 sync_pulse_stddev = stddev;
 126         }
 127         if (!quiet) {
 128                 fprintf(stderr, "Sync pulse length standard deviation: %.2f cycles\n",
 129                         sync_pulse_stddev);
 130         }
 131
 132         double sum = 0.0;
 133         for (int i = SYNC_PULSE_START; i < sync_pulse_end; ++i) {
 134                 sum += pulses[i].len;
 135         }
 136         double mean_length = C64_FREQUENCY * sum / (sync_pulse_end - SYNC_PULSE_START);
 137         double calibration_factor = SYNC_PULSE_LENGTH / mean_length;
 138         if (!quiet) {
 139                 fprintf(stderr, "Calibrated sync pulse length: %.2f -> %.2f (change %+.2f%%)\n",
 140                         mean_length, SYNC_PULSE_LENGTH, 100.0 * (calibration_factor - 1.0));
 141         }
 142
 143         // Check for pulses outside +/- 10% (sign of misdetection).
 144         for (int i = SYNC_PULSE_START; i < sync_pulse_end; ++i) {
 145                 double cycles = pulses[i].len * calibration_factor * C64_FREQUENCY;
 146                 if (cycles < SYNC_PULSE_LENGTH / SYNC_TEST_TOLERANCE || cycles > SYNC_PULSE_LENGTH * SYNC_TEST_TOLERANCE) {
 147                         fprintf(stderr, "Sync cycle with downflank at %.6f was detected at %.0f cycles; misdetect?\n",
 148                                 pulses[i].time, cycles);
 149                 }
 150         }
 151
 152         return calibration_factor;
 153 }
 154
 155 void output_tap(const std::vector<pulse>& pulses, double calibration_factor)
 156 {
 157         std::vector<char> tap_data;
 158         for (unsigned i = 0; i < pulses.size(); ++i) {
 159                 double cycles = pulses[i].len * calibration_factor * C64_FREQUENCY;
 160                 int len = lrintf(cycles / TAP_RESOLUTION);
 161                 if (i > SYNC_PULSE_END && (cycles < 100 || cycles > 800)) {
 162                         fprintf(stderr, "Cycle with downflank at %.6f was detected at %.0f cycles; misdetect?\n",
 163                                         pulses[i].time, cycles);
 164                 }
 165                 if (len <= 255) {
 166                         tap_data.push_back(len);
 167                 } else {
 168                         int overflow_len = lrintf(cycles);
 169                         tap_data.push_back(0);
 170                         tap_data.push_back(overflow_len & 0xff);
 171                         tap_data.push_back((overflow_len >> 8) & 0xff);
 172                         tap_data.push_back(overflow_len >> 16);
 173                 }
 174         }
 175
 176         tap_header hdr;
 177         memcpy(hdr.identifier, "C64-TAPE-RAW", 12);
 178         hdr.version = 1;
 179         hdr.reserved[0] = hdr.reserved[1] = hdr.reserved[2] = 0;
 180         hdr.data_len = tap_data.size();
 181
 182         fwrite(&hdr, sizeof(hdr), 1, stdout);
 183         fwrite(tap_data.data(), tap_data.size(), 1, stdout);
 184 }
 185
 186 static struct option long_options[] = {
 187         {"auto-level",       0,                 0, 'a' },
 188         {"auto-level-freq",  required_argument, 0, 'b' },
 189         {"output-leveled",   0,                 0, 'A' },
 190         {"min-level",        required_argument, 0, 'm' },
 191         {"no-calibrate",     0,                 0, 's' },
 192         {"plot-cycles",      0,                 0, 'p' },
 193         {"hysteresis-limit", required_argument, 0, 'l' },
 194         {"filter",           required_argument, 0, 'f' },
 195         {"rc-filter",        required_argument, 0, 'r' },
 196         {"output-filtered",  0,                 0, 'F' },
 197         {"crop",             required_argument, 0, 'c' },
 198         {"quiet",            0,                 0, 'q' },
 199         {"help",             0,                 0, 'h' },
 200         {0,                  0,                 0, 0   }
 201 };
 202
 203 void help()
 204 {
 205         fprintf(stderr, "decode [OPTIONS] AUDIO-FILE > TAP-FILE\n");
 206         fprintf(stderr, "\n");
 207         fprintf(stderr, "  -a, --auto-level             automatically adjust amplitude levels throughout the file\n");
 208         fprintf(stderr, "  -b, --auto-level-freq        minimum frequency in Hertz of corrected level changes (default 200 Hz)\n");
 209         fprintf(stderr, "  -A, --output-leveled         output leveled waveform to leveled.raw\n");
 210         fprintf(stderr, "  -m, --min-level              minimum estimated sound level (0..1) for --auto-level\n");
 211         fprintf(stderr, "  -s, --no-calibrate           do not try to calibrate on sync pulse length\n");
 212         fprintf(stderr, "  -p, --plot-cycles            output debugging info to cycles.plot\n");
 213         fprintf(stderr, "  -l, --hysteresis-limit U[:L] change amplitude threshold for ignoring pulses (-1..1)\n");
 214         fprintf(stderr, "  -f, --filter C1:C2:C3:...    specify FIR filter (up to %d coefficients)\n", NUM_FILTER_COEFF);
 215         fprintf(stderr, "  -r, --rc-filter FREQ         send signal through a highpass RC filter with given frequency (in Hertz)\n");
 216         fprintf(stderr, "  -F, --output-filtered        output filtered waveform to filtered.raw\n");
 217         fprintf(stderr, "  -c, --crop START[:END]       use only the given part of the file\n");
 218         fprintf(stderr, "  -t, --train LEN1:LEN2:...    train a filter for detecting any of the given number of cycles\n");
 219         fprintf(stderr, "                               (implies --no-calibrate and --quiet unless overridden)\n");
 220         fprintf(stderr, "  -q, --quiet                  suppress some informational messages\n");
 221         fprintf(stderr, "  -h, --help                   display this help, then exit\n");
 222         exit(1);
 223 }
 224
 225 void parse_options(int argc, char **argv)
 226 {
 227         for ( ;; ) {
 228                 int option_index = 0;
 229                 int c = getopt_long(argc, argv, "ab:Am:spl:f:r:Fc:t:qh", long_options, &option_index);
 230                 if (c == -1)
 231                         break;
 232
 233                 switch (c) {
 234                 case 'a':
 235                         do_auto_level = true;
 236                         break;
 237
 238                 case 'b':
 239                         auto_level_freq = atof(optarg);
 240                         break;
 241
 242                 case 'A':
 243                         output_leveled = true;
 244                         break;
 245
 246                 case 'm':
 247                         min_level = atof(optarg);
 248                         break;
 249
 250                 case 's':
 251                         do_calibrate = false;
 252                         break;
 253
 254                 case 'p':
 255                         output_cycles_plot = true;
 256                         break;
 257
 258                 case 'l': {
 259                         const char *hyststr = strtok(optarg, ": ");
 260                         hysteresis_upper_limit = atof(hyststr);
 261                         hyststr = strtok(NULL, ": ");
 262                         if (hyststr == NULL) {
 263                                 hysteresis_lower_limit = -hysteresis_upper_limit;
 264                         } else {
 265                                 hysteresis_lower_limit = atof(hyststr);
 266                         }
 267                         break;
 268                 }
 269
 270                 case 'f': {
 271                         const char *coeffstr = strtok(optarg, ": ");
 272                         int coeff_index = 0;
 273                         while (coeff_index < NUM_FILTER_COEFF && coeffstr != NULL) {
 274                                 filter_coeff[coeff_index++] = atof(coeffstr);
 275                                 coeffstr = strtok(NULL, ": ");
 276                         }
 277                         use_fir_filter = true;
 278                         break;
 279                 }
 280
 281                 case 'r':
 282                         use_rc_filter = true;
 283                         rc_filter_freq = atof(optarg);
 284                         break;
 285
 286                 case 'F':
 287                         output_filtered = true;
 288                         break;
 289
 290                 case 'c': {
 291                         const char *cropstr = strtok(optarg, ":");
 292                         crop_start = atof(cropstr);
 293                         cropstr = strtok(NULL, ":");
 294                         if (cropstr == NULL) {
 295                                 crop_end = HUGE_VAL;
 296                         } else {
 297                                 crop_end = atof(cropstr);
 298                         }
 299                         do_crop = true;
 300                         break;
 301                 }
 302
 303                 case 't': {
 304                         const char *cyclestr = strtok(optarg, ":");
 305                         while (cyclestr != NULL) {
 306                                 train_snap_points.push_back(atof(cyclestr));
 307                                 cyclestr = strtok(NULL, ":");
 308                         }
 309                         do_train = true;
 310
 311                         // Set reasonable defaults (can be overridden later on the command line).
 312                         do_calibrate = false;
 313                         quiet = true;
 314                         break;
 315                 }
 316
 317                 case 'q':
 318                         quiet = true;
 319                         break;
 320
 321                 case 'h':
 322                 default:
 323                         help();
 324                         exit(1);
 325                 }
 326         }
 327 }
 328
 329 std::vector<float> crop(const std::vector<float>& pcm, float crop_start, float crop_end, int sample_rate)
 330 {
 331         size_t start_sample, end_sample;
 332         if (crop_start >= 0.0f) {
 333                 start_sample = std::min<size_t>(lrintf(crop_start * sample_rate), pcm.size());
 334         }
 335         if (crop_end >= 0.0f) {
 336                 end_sample = std::min<size_t>(lrintf(crop_end * sample_rate), pcm.size());
 337         }
 338         return std::vector<float>(pcm.begin() + start_sample, pcm.begin() + end_sample);
 339 }
 340
 341 std::vector<float> do_fir_filter(const std::vector<float>& pcm, const float* filter)
 342 {
 343         std::vector<float> filtered_pcm;
 344         filtered_pcm.resize(pcm.size());
 345         unsigned i = NUM_FILTER_COEFF;
 346 #ifdef __AVX__
 347         unsigned avx_end = i + ((pcm.size() - i) & ~7);
 348         for ( ; i < avx_end; i += 8) {
 349                 __m256 s = _mm256_setzero_ps();
 350                 for (int j = 0; j < NUM_FILTER_COEFF; ++j) {
 351                         __m256 f = _mm256_set1_ps(filter[j]);
 352                         s = _mm256_fmadd_ps(f, _mm256_load_ps(&pcm[i - j]), s);
 353                 }
 354                 _mm256_storeu_ps(&filtered_pcm[i], s);
 355         }
 356 #endif
 357         // Do what we couldn't do with AVX (which is everything for non-AVX machines)
 358         // as scalar code.
 359         for (; i < pcm.size(); ++i) {
 360                 float s = 0.0f;
 361                 for (int j = 0; j < NUM_FILTER_COEFF; ++j) {
 362                         s += filter[j] * pcm[i - j];
 363                 }
 364                 filtered_pcm[i] = s;
 365         }
 366
 367         if (output_filtered) {
 368                 FILE *fp = fopen("filtered.raw", "wb");
 369                 fwrite(filtered_pcm.data(), filtered_pcm.size() * sizeof(filtered_pcm[0]), 1, fp);
 370                 fclose(fp);
 371         }
 372
 373         return filtered_pcm;
 374 }
 375
 376 std::vector<float> do_rc_filter(const std::vector<float>& pcm, float freq, int sample_rate)
 377 {
 378         // This is only a 6 dB/oct filter, which seemingly works better
 379         // than the Filter class, which is a standard biquad (12 dB/oct).
 380         // The b/c calculations come from libnyquist (atone.c);
 381         // I haven't checked, but I suppose they fall out of the bilinear
 382         // transform of the transfer function H(s) = s/(s + w).
 383         std::vector<float> filtered_pcm;
 384         filtered_pcm.resize(pcm.size());
 385         const float b = 2.0f - cos(2.0 * M_PI * freq / sample_rate);
 386         const float c = b - sqrt(b * b - 1.0f);
 387         float prev_in = 0.0f;
 388         float prev_out = 0.0f;
 389         for (unsigned i = 0; i < pcm.size(); ++i) {
 390                 float in = pcm[i];
 391                 float out = c * (prev_out + in - prev_in);
 392                 filtered_pcm[i] = out;
 393                 prev_in = in;
 394                 prev_out = out;
 395         }
 396
 397         if (output_filtered) {
 398                 FILE *fp = fopen("filtered.raw", "wb");
 399                 fwrite(filtered_pcm.data(), filtered_pcm.size() * sizeof(filtered_pcm[0]), 1, fp);
 400                 fclose(fp);
 401         }
 402
 403         return filtered_pcm;
 404 }
 405
 406 template<bool fast>
 407 std::vector<pulse> detect_pulses(const std::vector<float> &pcm, float hysteresis_upper_limit, float hysteresis_lower_limit, int sample_rate)
 408 {
 409         std::vector<pulse> pulses;
 410
 411         // Find the flanks.
 412         enum State { START, ABOVE, BELOW } state = START;
 413         double last_downflank = -1;
 414         for (unsigned i = 0; i < pcm.size(); ++i) {
 415                 if (pcm[i] > hysteresis_upper_limit) {
 416                         state = ABOVE;
 417                 } else if (pcm[i] < hysteresis_lower_limit) {
 418                         if (state == ABOVE) {
 419                                 // down-flank!
 420                                 double t = find_crossing<fast>(pcm, i - 1, hysteresis_lower_limit) * (1.0 / sample_rate) + crop_start;
 421                                 if (last_downflank > 0) {
 422                                         pulse p;
 423                                         p.time = t;
 424                                         p.len = t - last_downflank;
 425                                         pulses.push_back(p);
 426                                 }
 427                                 last_downflank = t;
 428                         }
 429                         state = BELOW;
 430                 }
 431         }
 432         return pulses;
 433 }
 434
 435 void output_cycle_plot(const std::vector<pulse> &pulses, double calibration_factor)
 436 {
 437         FILE *fp = fopen("cycles.plot", "w");
 438         for (unsigned i = 0; i < pulses.size(); ++i) {
 439                 double cycles = pulses[i].len * calibration_factor * C64_FREQUENCY;
 440                 fprintf(fp, "%f %f\n", pulses[i].time, cycles);
 441         }
 442         fclose(fp);
 443 }
 444
 445 std::pair<int, double> find_closest_point(double x, const std::vector<float> &points)
 446 {
 447         int best_point = 0;
 448         double best_dist = (x - points[0]) * (x - points[0]);
 449         for (unsigned j = 1; j < train_snap_points.size(); ++j) {
 450                 double dist = (x - points[j]) * (x - points[j]);
 451                 if (dist < best_dist) {
 452                         best_point = j;
 453                         best_dist = dist;
 454                 }
 455         }
 456         return std::make_pair(best_point, best_dist);
 457 }
 458
 459 float eval_badness(const std::vector<pulse>& pulses, double calibration_factor)
 460 {
 461         double sum_badness = 0.0;
 462         for (unsigned i = 0; i < pulses.size(); ++i) {
 463                 double cycles = pulses[i].len * calibration_factor * C64_FREQUENCY;
 464                 if (cycles > 2000.0) cycles = 2000.0;  // Don't make pauses arbitrarily bad.
 465                 std::pair<int, double> selected_point_and_sq_dist = find_closest_point(cycles, train_snap_points);
 466                 sum_badness += selected_point_and_sq_dist.second;
 467         }
 468         return sqrt(sum_badness / (pulses.size() - 1));
 469 }
 470
 471 void find_kmeans(const std::vector<pulse> &pulses, double calibration_factor, const std::vector<float> &initial_centers)
 472 {
 473         std::vector<float> last_centers = initial_centers;
 474         std::vector<float> sums;
 475         std::vector<float> num;
 476         sums.resize(initial_centers.size());
 477         num.resize(initial_centers.size());
 478         for ( ;; ) {
 479                 for (unsigned i = 0; i < initial_centers.size(); ++i) {
 480                         sums[i] = 0.0f;
 481                         num[i] = 0;
 482                 }
 483                 for (unsigned i = 0; i < pulses.size(); ++i) {
 484                         double cycles = pulses[i].len * calibration_factor * C64_FREQUENCY;
 485                         // Ignore heavy outliers, which are almost always long pauses.
 486                         if (cycles > 2000.0) {
 487                                 continue;
 488                         }
 489                         std::pair<int, double> selected_point_and_sq_dist = find_closest_point(cycles, last_centers);
 490                         int p = selected_point_and_sq_dist.first;
 491                         sums[p] += cycles;
 492                         ++num[p];
 493                 }
 494                 bool any_moved = false;
 495                 for (unsigned i = 0; i < initial_centers.size(); ++i) {
 496                         if (num[i] == 0) {
 497                                 fprintf(stderr, "K-means broke down, can't output new reference training points\n");
 498                                 return;
 499                         }
 500                         float new_center = sums[i] / num[i];
 501                         if (fabs(new_center - last_centers[i]) > 1e-3) {
 502                                 any_moved = true;
 503                         }
 504                         last_centers[i] = new_center;
 505                 }
 506                 if (!any_moved) {
 507                         break;
 508                 }
 509         }
 510         fprintf(stderr, "New reference training points:");
 511         for (unsigned i = 0; i < last_centers.size(); ++i) {
 512                 fprintf(stderr, " %.3f", last_centers[i]);
 513         }
 514         fprintf(stderr, "\n");
 515 }
 516
 517 void spsa_train(const std::vector<float> &pcm, int sample_rate)
 518 {
 519         float vals[NUM_SPSA_VALS] = { hysteresis_upper_limit, hysteresis_lower_limit, 1.0f };  // The rest is filled with 0.
 520
 521         float start_c = INITIAL_C;
 522         double best_badness = HUGE_VAL;
 523
 524         for (int n = 1; n < NUM_ITER; ++n) {
 525                 float a = INITIAL_A * pow(n + A, -ALPHA);
 526                 float c = start_c * pow(n, -GAMMA);
 527
 528                 // find a random perturbation
 529                 float p[NUM_SPSA_VALS];
 530                 float vals1[NUM_SPSA_VALS], vals2[NUM_SPSA_VALS];
 531                 for (int i = 0; i < NUM_SPSA_VALS; ++i) {
 532                         p[i] = (rand() % 2) ? 1.0 : -1.0;
 533                         vals1[i] = std::max(std::min(vals[i] - c * p[i], 1.0f), -1.0f);
 534                         vals2[i] = std::max(std::min(vals[i] + c * p[i], 1.0f), -1.0f);
 535                 }
 536
 537                 std::vector<pulse> pulses1 = detect_pulses<true>(do_fir_filter(pcm, vals1 + 2), vals1[0], vals1[1], sample_rate);
 538                 std::vector<pulse> pulses2 = detect_pulses<true>(do_fir_filter(pcm, vals2 + 2), vals2[0], vals2[1], sample_rate);
 539                 float badness1 = eval_badness(pulses1, 1.0);
 540                 float badness2 = eval_badness(pulses2, 1.0);
 541
 542                 // Find the gradient estimator
 543                 float g[NUM_SPSA_VALS];
 544                 for (int i = 0; i < NUM_SPSA_VALS; ++i) {
 545                         g[i] = (badness2 - badness1) / (2.0 * c * p[i]);
 546                         vals[i] -= a * g[i];
 547                         vals[i] = std::max(std::min(vals[i], 1.0f), -1.0f);
 548                 }
 549                 if (badness2 < badness1) {
 550                         std::swap(badness1, badness2);
 551                         std::swap(vals1, vals2);
 552                         std::swap(pulses1, pulses2);
 553                 }
 554                 if (badness1 < best_badness) {
 555                         fprintf(stderr, "\nNew best filter (badness=%f):", badness1);
 556                         for (int i = 0; i < NUM_FILTER_COEFF; ++i) {
 557                                 fprintf(stderr, " %.5f", vals1[i + 2]);
 558                         }
 559                         fprintf(stderr, ", hysteresis limits = %f %f\n", vals1[0], vals1[1]);
 560                         best_badness = badness1;
 561
 562                         find_kmeans(pulses1, 1.0, train_snap_points);
 563
 564                         if (output_cycles_plot) {
 565                                 output_cycle_plot(pulses1, 1.0);
 566                         }
 567                 }
 568                 fprintf(stderr, "%d ", n);
 569                 fflush(stderr);
 570         }
 571 }
 572
 573 int main(int argc, char **argv)
 574 {
 575         parse_options(argc, argv);
 576
 577         make_lanczos_weight_table();
 578         std::vector<float> pcm;
 579         int sample_rate;
 580         if (!read_audio_file(argv[optind], &pcm, &sample_rate)) {
 581                 exit(1);
 582         }
 583
 584         if (do_crop) {
 585                 pcm = crop(pcm, crop_start, crop_end, sample_rate);
 586         }
 587
 588         if (use_fir_filter) {
 589                 pcm = do_fir_filter(pcm, filter_coeff);
 590         }
 591
 592         if (use_rc_filter) {
 593                 pcm = do_rc_filter(pcm, rc_filter_freq, sample_rate);
 594         }
 595
 596         if (do_auto_level) {
 597                 pcm = level_samples(pcm, min_level, auto_level_freq, sample_rate);
 598                 if (output_leveled) {
 599                         FILE *fp = fopen("leveled.raw", "wb");
 600                         fwrite(pcm.data(), pcm.size() * sizeof(pcm[0]), 1, fp);
 601                         fclose(fp);
 602                 }
 603         }
 604
 605 #if 0
 606         for (int i = 0; i < LEN; ++i) {
 607                 in[i] += rand() % 10000;
 608         }
 609 #endif
 610
 611 #if 0
 612         for (int i = 0; i < LEN; ++i) {
 613                 printf("%d\n", in[i]);
 614         }
 615 #endif
 616
 617         if (do_train) {
 618                 spsa_train(pcm, sample_rate);
 619                 exit(0);
 620         }
 621
 622         std::vector<pulse> pulses = detect_pulses<false>(pcm, hysteresis_upper_limit, hysteresis_lower_limit, sample_rate);
 623
 624         double calibration_factor = 1.0;
 625         if (do_calibrate) {
 626                 calibration_factor = calibrate(pulses);
 627         }
 628
 629         if (output_cycles_plot) {
 630                 output_cycle_plot(pulses, calibration_factor);
 631         }
 632
 633         output_tap(pulses, calibration_factor);
 634 }