I’m using Arduino nano 33 ble sense to create a multifunctional home smart system, the system has two main functionalities: Voice recognition for the phrases “lights on” and “lights off” using machine learning and a fire detection system using a fire flame sensor. The voice recognition part is working fine for me, however for the fire detection system I’m trying to use an interrupt to trigger a buzzer once a fire is detected and for some reason the interrupt is not working at all. Can you please guide me on what wrong am i doing?
My code:
#include <PDM.h>
#include <embedded-systems_inferencing.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
static byte light = 13;
static byte buzzer = 8;
const byte interruptPin = 2;
/**
* @brief Arduino setup function
*/
void setup()
{
// put your setup code here, to run once:
Serial.begin(115200);
pinMode(buzzer, OUTPUT);
attachInterrupt(digitalPinToInterrupt(interruptPin), fire, RISING);
// comment out the below line to cancel the wait for USB connection (needed for native USB)
while (!Serial);
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: Could not allocate audio buffer (size %d), this could be due to the window length of your model\r\n", EI_CLASSIFIER_RAW_SAMPLE_COUNT);
return;
}
}
/**
* @brief Arduino main function. Runs the inferencing loop.
*/
void loop()
{
int label=0;
float best_prediction=0;
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 = µphone_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 > best_prediction){
best_prediction =result.classification[ix].value;
label=ix;}
}
ei_printf("final prediction: %s\n",result.classification[label].label);
// get the prediciton result and implement the command
if (result.classification[label].label == "lightson"){
digitalWrite(light, HIGH);}
else if (result.classification[label].label == "lightsoff"){
digitalWrite(light, LOW);
}
#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);
}
static void notify_user(){
// send a notification to the user about the fire
}
static void fire(){
// trigger the buzzer in case of fire + send a notification to the user about the fire
ei_printf("FIRE DETECTED");
digitalWrite(buzzer, HIGH); // Send 1KHz sound signal...
notify_user();
}
#if !defined(EI_CLASSIFIER_SENSOR) || EI_CLASSIFIER_SENSOR != EI_CLASSIFIER_SENSOR_MICROPHONE
#error "Invalid model for current sensor."
#endif