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7862a098588612ad08dbeb8cdf0ae4012cce1eee
buzz_detector/main/calibration.c
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mitchell 7862a09858 vibe code the initial impl
2026-10-13 19:47:46 -05:00

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// learn / adapt / selfcheck / test / background / autothresh.
// A session is started by a console task, fed by the processing task via
// calibration_on_frame(), and finished (mode -> NORMAL) by the processing task, which
// then signals the waiting console task. Session data is guarded by s_mx.
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "esp_log.h"
#include "freertos/FreeRTOS.h"
#include "freertos/semphr.h"
#include "calibration.h"
#include "config_store.h"
#include "fingerprint.h"
#include "matcher.h"
#define LEARN_SECS 60
#define TEST_SECS 10
#define BG_SECS 60
#define SECS_TO_FRAMES(s) ((uint32_t)(s) * FS_HZ / HOP_LEN)
#define NOISE_FRAMES (FS_HZ / HOP_LEN) // first second -> noise floor
#define MIN_LEARN_FRAMES 20
#define MAX_SCORES 2048
_Static_assert(BG_SECS * FS_HZ / HOP_LEN <= MAX_SCORES, "score buffer too small for background run");
static SemaphoreHandle_t s_mx;
static SemaphoreHandle_t s_done;
static volatile app_mode_t s_mode = MODE_NORMAL;
static struct {
uint32_t frame_idx, total_frames;
float min_rms, threshold, noise_mult;
// learn / adapt
float noise[NOISE_FRAMES];
float gate;
float fp_sum[NUM_BANDS];
uint32_t count;
// test / background
float scores[MAX_SCORES];
uint32_t n_scores;
uint32_t n_gated, n_above, n_above_gated;
} S;
static score_stats_t s_bg, s_buzz;
static int cmp_f(const void *a, const void *b)
{
float x = *(const float *)a, y = *(const float *)b;
return (x > y) - (x < y);
}
static float pct(const float *sorted, uint32_t n, float p)
{
// Linear interpolation, same as numpy.percentile default.
float pos = p / 100.0f * (float)(n - 1);
uint32_t lo = (uint32_t)pos;
uint32_t hi = lo + 1 < n ? lo + 1 : lo;
float fr = pos - (float)lo;
return sorted[lo] * (1.0f - fr) + sorted[hi] * fr;
}
static void compute_stats(float *a, uint32_t n, score_stats_t *o)
{
memset(o, 0, sizeof(*o));
if (n == 0) return;
qsort(a, n, sizeof(float), cmp_f);
double sum = 0;
for (uint32_t i = 0; i < n; i++) sum += a[i];
o->valid = true;
o->n = n;
o->min = a[0];
o->max = a[n - 1];
o->mean = (float)(sum / n);
o->p5 = pct(a, n, 5);
o->p50 = pct(a, n, 50);
o->p95 = pct(a, n, 95);
o->p99 = pct(a, n, 99);
}
static void print_stats(const char *label, const score_stats_t *s)
{
printf("%s (n=%lu): min=%.3f p5=%.3f p50=%.3f mean=%.3f p95=%.3f p99=%.3f max=%.3f\n",
label, (unsigned long)s->n, s->min, s->p5, s->p50, s->mean, s->p95, s->p99, s->max);
}
esp_err_t calibration_init(void)
{
s_mx = xSemaphoreCreateMutex();
s_done = xSemaphoreCreateBinary();
return (s_mx && s_done) ? ESP_OK : ESP_ERR_NO_MEM;
}
app_mode_t calibration_get_mode(void) { return s_mode; }
const char *calibration_mode_name(app_mode_t m)
{
switch (m) {
case MODE_NORMAL: return "NORMAL";
case MODE_LEARNING: return "LEARNING";
case MODE_ADAPTING: return "ADAPTING";
case MODE_BACKGROUND_CAL: return "BACKGROUND_CAL";
case MODE_TEST_CAL: return "TEST_CAL";
}
return "?";
}
bool calibration_get_background(score_stats_t *o) { *o = s_bg; return s_bg.valid; }
bool calibration_get_buzz(score_stats_t *o) { *o = s_buzz; return s_buzz.valid; }
// ---- frame feed (processing task) -------------------------------------------
void calibration_on_frame(const float fp[NUM_BANDS], bool fp_valid, float rms, float score)
{
xSemaphoreTake(s_mx, portMAX_DELAY);
app_mode_t m = s_mode;
if (m == MODE_NORMAL) {
xSemaphoreGive(s_mx);
return;
}
uint32_t i = S.frame_idx++;
switch (m) {
case MODE_LEARNING:
case MODE_ADAPTING:
if (i < NOISE_FRAMES) {
S.noise[i] = rms;
if (i == NOISE_FRAMES - 1) {
float tmp[NOISE_FRAMES];
memcpy(tmp, S.noise, sizeof(tmp));
qsort(tmp, NOISE_FRAMES, sizeof(float), cmp_f);
float floor_rms = tmp[NOISE_FRAMES / 2]; // median
float g = floor_rms * S.noise_mult;
S.gate = g > S.min_rms ? g : S.min_rms; // never below the detector gate
}
} else if (fp_valid && rms >= S.gate) {
for (int b = 0; b < NUM_BANDS; b++) S.fp_sum[b] += fp[b];
S.count++;
}
break;
case MODE_TEST_CAL:
if (fp_valid && rms >= S.min_rms && S.n_scores < MAX_SCORES) {
S.scores[S.n_scores++] = score;
}
break;
case MODE_BACKGROUND_CAL:
if (S.n_scores < MAX_SCORES) S.scores[S.n_scores++] = fp_valid ? score : 0.0f;
if (rms >= S.min_rms) S.n_gated++;
if (score >= S.threshold) {
S.n_above++;
if (rms >= S.min_rms) S.n_above_gated++;
}
break;
default:
break;
}
if (S.frame_idx >= S.total_frames) {
s_mode = MODE_NORMAL;
xSemaphoreGive(s_done);
}
xSemaphoreGive(s_mx);
}
// ---- session control (console / button tasks) --------------------------------
static esp_err_t begin(app_mode_t m, uint32_t secs)
{
app_config_t cfg;
config_get(&cfg);
xSemaphoreTake(s_mx, portMAX_DELAY);
if (s_mode != MODE_NORMAL) {
xSemaphoreGive(s_mx);
printf("busy: a %s session is already running\n", calibration_mode_name(s_mode));
return ESP_ERR_INVALID_STATE;
}
memset(&S, 0, sizeof(S));
S.total_frames = SECS_TO_FRAMES(secs);
S.min_rms = cfg.min_rms;
S.threshold = cfg.threshold;
S.noise_mult = cfg.noise_mult;
xSemaphoreTake(s_done, 0);
s_mode = m; // last: processing task starts feeding after this
xSemaphoreGive(s_mx);
return ESP_OK;
}
static esp_err_t wait_done(uint32_t secs)
{
for (uint32_t t = 1; t <= secs + 10; t++) {
if (xSemaphoreTake(s_done, pdMS_TO_TICKS(1000)) == pdTRUE) {
printf("\n");
return ESP_OK;
}
printf(" %lus", (unsigned long)t);
fflush(stdout);
}
xSemaphoreTake(s_mx, portMAX_DELAY);
s_mode = MODE_NORMAL;
xSemaphoreGive(s_mx);
printf("\nsession timed out: no audio frames arriving?\n");
return ESP_ERR_TIMEOUT;
}
esp_err_t calibration_run_learn(bool adapt, float w_new)
{
if (adapt && !matcher_has_template()) {
printf("adapt needs an existing template; use `learn` first\n");
return ESP_ERR_INVALID_STATE;
}
if (adapt && !(w_new >= 0.0f && w_new <= 1.0f)) {
printf("adapt weight must be 0..1\n");
return ESP_ERR_INVALID_ARG;
}
esp_err_t err = begin(adapt ? MODE_ADAPTING : MODE_LEARNING, LEARN_SECS);
if (err != ESP_OK) return err;
printf("%s: %d s. Stay QUIET for the first second, then press the buzzer a few times.\n",
adapt ? "ADAPT" : "LEARN", LEARN_SECS);
err = wait_done(LEARN_SECS);
if (err != ESP_OK) return err;
xSemaphoreTake(s_mx, portMAX_DELAY);
uint32_t count = S.count;
float gate = S.gate;
float v[NUM_BANDS];
for (int b = 0; b < NUM_BANDS; b++) v[b] = count ? S.fp_sum[b] / (float)count : 0.0f;
xSemaphoreGive(s_mx);
printf("noise-gate RMS %.1f, loud frames collected: %lu\n", gate, (unsigned long)count);
if (count < MIN_LEARN_FRAMES) {
printf("REFUSED: need >= %d loud frames. Press the buzzer longer / closer, or lower noise_mult/min_rms.\n",
MIN_LEARN_FRAMES);
return ESP_FAIL;
}
// |mean of unit vectors| = average cosine of the frames to their own mean direction.
float s2 = 0;
for (int b = 0; b < NUM_BANDS; b++) s2 += v[b] * v[b];
printf("cohesion of collected frames: %.3f (close to 1 = consistent sound)\n", sqrtf(s2));
if (!features_l2_normalize(v, NUM_BANDS)) {
printf("REFUSED: collected fingerprint is degenerate (flat)\n");
return ESP_FAIL;
}
if (adapt) {
float cur[NUM_BANDS];
matcher_get_template(cur);
printf("similarity new vs current template: %.3f\n", features_dot(v, cur));
for (int b = 0; b < NUM_BANDS; b++) v[b] = w_new * v[b] + (1.0f - w_new) * cur[b];
if (!features_l2_normalize(v, NUM_BANDS)) {
printf("REFUSED: blended template degenerate\n");
return ESP_FAIL;
}
}
err = matcher_set_template(v, count);
if (err != ESP_OK) {
printf("saving template failed: %s\n", esp_err_to_name(err));
return err;
}
printf("%s done: template saved to NVS (source now: %s)%s\n", adapt ? "ADAPT" : "LEARN",
matcher_source_name(), adapt ? "" : ". Run `selfcheck` to verify.");
return ESP_OK;
}
esp_err_t calibration_run_test(bool verdict)
{
if (!matcher_has_template()) {
printf("no template: run make_template.py or `learn` first\n");
return ESP_ERR_INVALID_STATE;
}
esp_err_t err = begin(MODE_TEST_CAL, TEST_SECS);
if (err != ESP_OK) return err;
printf("%s: %d s. Press the real buzzer now (several presses).\n", verdict ? "SELFCHECK" : "TEST", TEST_SECS);
err = wait_done(TEST_SECS);
if (err != ESP_OK) return err;
xSemaphoreTake(s_mx, portMAX_DELAY);
uint32_t n = S.n_scores;
float thr = S.threshold, gate = S.min_rms;
score_stats_t st;
compute_stats(S.scores, n, &st);
xSemaphoreGive(s_mx);
printf("loud frames (RMS >= %.0f): %lu\n", gate, (unsigned long)n);
if (n < MIN_LEARN_FRAMES) {
printf("WARNING: only %lu loud frames; too few for reliable stats (press longer or lower min_rms).\n",
(unsigned long)n);
if (n == 0) return ESP_FAIL;
} else {
s_buzz = st; // used by autothresh
}
print_stats("score vs active template", &st);
if (verdict && n >= MIN_LEARN_FRAMES) {
if (st.p5 >= thr) {
printf("VERDICT: GOOD match. 95%% of loud frames clear threshold %.2f.\n", thr);
} else if (st.p50 >= thr) {
printf("VERDICT: WEAK match. The median frame clears %.2f but the tail does not.\n"
" Try `adapt` (blend with live mic) or `learn`, then `selfcheck` again.\n", thr);
} else {
printf("VERDICT: POOR match (median %.2f < threshold %.2f).\n"
" Was that really the buzzer? If yes: `learn` on this mic, or see README about FP_DETREND\n"
" (compile-time, feature_params.h) if mic tilt is the issue.\n", st.p50, thr);
}
}
return ESP_OK;
}
esp_err_t calibration_run_background(void)
{
if (!matcher_has_template()) {
printf("no template: run make_template.py or `learn` first\n");
return ESP_ERR_INVALID_STATE;
}
esp_err_t err = begin(MODE_BACKGROUND_CAL, BG_SECS);
if (err != ESP_OK) return err;
printf("BACKGROUND: %d s of normal ambient sound (do NOT press the buzzer).\n", BG_SECS);
err = wait_done(BG_SECS);
if (err != ESP_OK) return err;
xSemaphoreTake(s_mx, portMAX_DELAY);
uint32_t n = S.n_scores, gated = S.n_gated, above = S.n_above, above_g = S.n_above_gated;
float thr = S.threshold;
compute_stats(S.scores, n, &s_bg);
xSemaphoreGive(s_mx);
print_stats("background score vs active template", &s_bg);
printf("frames passing the loudness gate: %lu / %lu\n", (unsigned long)gated, (unsigned long)n);
printf("frames with score >= threshold %.2f: %lu (of which also loud enough to match: %lu)\n",
thr, (unsigned long)above, (unsigned long)above_g);
return ESP_OK;
}
esp_err_t calibration_autothresh(void)
{
if (!s_bg.valid || !s_buzz.valid) {
printf("need both: `background` and (`test` or `selfcheck`) with >= %d loud frames first\n", MIN_LEARN_FRAMES);
return ESP_ERR_INVALID_STATE;
}
float lo = s_bg.p99, hi = s_buzz.p5;
float thr = 0.5f * (lo + hi);
if (thr < 0.50f) thr = 0.50f;
if (thr > 0.98f) thr = 0.98f;
printf("background p99 = %.3f, buzzer p5 = %.3f -> threshold %.3f\n", lo, hi, thr);
if (hi <= lo) {
printf("WARNING: distributions OVERLAP. Expect false alarms or missed buzzes. "
"Improve the template (`learn`), move the mic, or revisit FP_DETREND.\n");
}
char msg[96], val[24];
snprintf(val, sizeof(val), "%.4f", thr);
if (config_set_param("threshold", val, msg, sizeof(msg)) != ESP_OK) {
printf("could not set threshold: %s\n", msg);
return ESP_FAIL;
}
printf("threshold set and saved.\n");
return ESP_OK;
}
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