Who knocks my door?

#include <ArduinoBLE.h>
#include <knocking_door_project_inferencing.h>
#include <PDM.h>

/** Audio buffers, pointers and selectors */
typedef struct {
    int16_t *buffer;
    uint8_t buf_ready;
    uint32_t buf_count;
    uint32_t n_samples;
} inference_t;

static inference_t inference;
static signed short sampleBuffer[2048];
static bool debug_nn = false; // Set this to true to see e.g. features generated from the raw signal

BLEDevice central;
BLEService yourService("cb006965-29ec-4727-92cd-5ad8bdc2d3c7");
BLEUnsignedCharCharacteristic yourClass("154c7f82-d8bf-4589-86ec-61c77d820763", BLERead | BLENotify);

  int detectedClassValue = 0;
  int detectedClass = 0;

  int conversation;
  int doorbell;
  int house_silence;
  int knocking_door;
  int tv_sound;

/**
 * @brief      Arduino setup function
 */
void setup()
{
    // put your setup code here, to run once:
    Serial.begin(115200);

    Serial.println("Edge Impulse Inferencing Demo");

    // summary of inferencing settings (from model_metadata.h)
    ei_printf("Inferencing settings:\n");
    ei_printf("\tInterval: %.2f ms.\n", (float)EI_CLASSIFIER_INTERVAL_MS);
    ei_printf("\tFrame size: %d\n", EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE);
    ei_printf("\tSample length: %d ms.\n", EI_CLASSIFIER_RAW_SAMPLE_COUNT / 16);
    ei_printf("\tNo. of classes: %d\n", sizeof(ei_classifier_inferencing_categories) / sizeof(ei_classifier_inferencing_categories[0]));

    if (microphone_inference_start(EI_CLASSIFIER_RAW_SAMPLE_COUNT) == false) {
        ei_printf("ERR: Failed to setup audio sampling\r\n");
        return;
    }

    if (!BLE.begin()) {
      Serial.println("starting BLE failed!");
      while (1);
    }
    
    pinMode(LED_BUILTIN, OUTPUT);
    pinMode(LEDB, OUTPUT);
    pinMode(LEDR, OUTPUT);

    BLE.setDeviceName("knocking_door"); 
    BLE.setLocalName("knocking_door");
    BLE.setAdvertisedService(yourService);
    yourService.addCharacteristic(yourClass);
    BLE.addService(yourService); 
    BLE.advertise();

    Serial.println("Bluetooth device active, waiting for connections...");

    while (1) {
      central = BLE.central();
      if (central) {
        Serial.print("Connected to central: ");
        Serial.println(central.address());
        digitalWrite(LED_BUILTIN, HIGH);
        break;
      }
    }
}

/**
 * @brief      Arduino main function. Runs the inferencing loop.
 */
void loop()
{
    ei_printf("Starting inferencing in 2 seconds...\n");

    delay(2000);

    ei_printf("Recording...\n");

    bool m = microphone_inference_record();
    if (!m) {
        ei_printf("ERR: Failed to record audio...\n");
        return;
    }

    ei_printf("Recording done\n");

    signal_t signal;
    signal.total_length = EI_CLASSIFIER_RAW_SAMPLE_COUNT;
    signal.get_data = &microphone_audio_signal_get_data;
    ei_impulse_result_t result = { 0 };

    EI_IMPULSE_ERROR r = run_classifier(&signal, &result, debug_nn);
    if (r != EI_IMPULSE_OK) {
        ei_printf("ERR: Failed to run classifier (%d)\n", r);
        return;
    }

    // print the predictions
    ei_printf("Predictions ");
    ei_printf("(DSP: %d ms., Classification: %d ms., Anomaly: %d ms.)",
        result.timing.dsp, result.timing.classification, result.timing.anomaly);
    ei_printf(": \n");
    for (size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++) {
        ei_printf("    %s: %.5f\n", result.classification[ix].label, result.classification[ix].value);

        if (result.classification[ix].value > detectedClassValue) {
           detectedClassValue = result.classification[ix].value;
           detectedClass = ix;
        }
    }

    conversation = result.classification[0].value*100;
    doorbell = result.classification[1].value*100;
    house_silence = result.classification[2].value*100;
    knocking_door = result.classification[3].value*100;
    tv_sound = result.classification[4].value*100;

    digitalWrite(LEDB, HIGH);
    digitalWrite(LEDR, HIGH);
    
    if (knocking_door > 30) {
        digitalWrite(LEDB, LOW);
        digitalWrite(LEDR, HIGH);
        digitalWrite(LEDG, HIGH);
        delay(200);
        digitalWrite(LEDB, HIGH);
        delay(100);
        digitalWrite(LEDB, LOW);
        digitalWrite(LEDR, HIGH);
        digitalWrite(LEDG, HIGH);
        delay(200);
        digitalWrite(LEDB, HIGH);
        delay(100);
        digitalWrite(LEDB, LOW);
        digitalWrite(LEDR, HIGH);
        digitalWrite(LEDG, HIGH);
        delay(200);
        digitalWrite(LEDB, HIGH);
        delay(100);
    }

    if (doorbell > 30) {
        digitalWrite(LEDB, HIGH);
        digitalWrite(LEDR, HIGH);
        digitalWrite(LEDG, LOW);
        delay(200);
        digitalWrite(LEDG, HIGH);
        delay(100);
        digitalWrite(LEDB, HIGH);
        digitalWrite(LEDR, HIGH);
        digitalWrite(LEDG, LOW);
        delay(200);
        digitalWrite(LEDG, HIGH);
        delay(100);
        digitalWrite(LEDB, HIGH);
        digitalWrite(LEDR, HIGH);
        digitalWrite(LEDG, LOW);
        delay(200);
        digitalWrite(LEDG, HIGH);
        delay(100);
    }
    
    if (central.connected()) yourClass.writeValue(detectedClass);
    
#if EI_CLASSIFIER_HAS_ANOMALY == 1
    ei_printf("    anomaly score: %.3f\n", result.anomaly);
#endif
}

/**
 * @brief      PDM buffer full callback
 *             Get data and call audio thread callback
 */
static void pdm_data_ready_inference_callback(void)
{
    int bytesAvailable = PDM.available();

    // read into the sample buffer
    int bytesRead = PDM.read((char *)&sampleBuffer[0], bytesAvailable);

    if (inference.buf_ready == 0) {
        for(int i = 0; i < bytesRead>>1; i++) {
            inference.buffer[inference.buf_count++] = sampleBuffer[i];

            if(inference.buf_count >= inference.n_samples) {
                inference.buf_count = 0;
                inference.buf_ready = 1;
                break;
            }
        }
    }
}

/**
 * @brief      Init inferencing struct and setup/start PDM
 *
 * @param[in]  n_samples  The n samples
 *
 * @return     { description_of_the_return_value }
 */
static bool microphone_inference_start(uint32_t n_samples)
{
    inference.buffer = (int16_t *)malloc(n_samples * sizeof(int16_t));

    if(inference.buffer == NULL) {
        return false;
    }

    inference.buf_count  = 0;
    inference.n_samples  = n_samples;
    inference.buf_ready  = 0;

    // configure the data receive callback
    PDM.onReceive(&pdm_data_ready_inference_callback);

    PDM.setBufferSize(4096);

    // initialize PDM with:
    // - one channel (mono mode)
    // - a 16 kHz sample rate
    if (!PDM.begin(1, EI_CLASSIFIER_FREQUENCY)) {
        ei_printf("Failed to start PDM!");
        microphone_inference_end();

        return false;
    }

    // set the gain, defaults to 20
    PDM.setGain(127);

    return true;
}

/**
 * @brief      Wait on new data
 *
 * @return     True when finished
 */
static bool microphone_inference_record(void)
{
    inference.buf_ready = 0;
    inference.buf_count = 0;

    while(inference.buf_ready == 0) {
        delay(10);
    }

    return true;
}

/**
 * Get raw audio signal data
 */
static int microphone_audio_signal_get_data(size_t offset, size_t length, float *out_ptr)
{
    numpy::int16_to_float(&inference.buffer[offset], out_ptr, length);

    return 0;
}

/**
 * @brief      Stop PDM and release buffers
 */
static void microphone_inference_end(void)
{
    PDM.end();
    free(inference.buffer);
}

#if !defined(EI_CLASSIFIER_SENSOR) || EI_CLASSIFIER_SENSOR != EI_CLASSIFIER_SENSOR_MICROPHONE
#error "Invalid model for current sensor."
#endif