static std::vector<float> 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<float> &pcm, int x)
{
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' },
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");
{
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;
output_leveled = true;
break;
+ case 'm':
+ min_level = atof(optarg) / 32768.0;
+ break;
+
case 's':
do_calibrate = false;
break;
fclose(fp);
}
+std::pair<int, double> find_closest_point(double x, const std::vector<float> &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<pulse>& 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<int, double> 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<float> &pcm, int sample_rate)
+void find_kmeans(const std::vector<pulse> &pulses, double calibration_factor, const std::vector<float> &initial_centers)
+{
+ std::vector<float> last_centers = initial_centers;
+ std::vector<float> sums;
+ std::vector<float> 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<int, double> 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<float> &pcm, int sample_rate)
{
- // Train!
float filter[NUM_FILTER_COEFF] = { 1.0f }; // The rest is filled with 0.
float start_c = INITIAL_C;
best_badness = badness1;
printf("\n");
+ find_kmeans(pulses1, 1.0, train_snap_points);
+
if (output_cycles_plot) {
output_cycle_plot(pulses1, 1.0);
}
}
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);