Puppi

PUPPI is a tiny, portable, edge ML device ready to interpret a dog's mood based on vocal signals.

BeginnerWork in progress679
Puppi

Things used in this project

Hardware components

Arduino Nano 33 BLE Sense
Arduino Nano 33 BLE Sense
×1

Software apps and online services

Arduino IDE
Arduino IDE
MIT App Inventor
MIT App Inventor
Edge Impulse Studio
Edge Impulse Studio

Hand tools and fabrication machines

3D Printer (generic)
3D Printer (generic)

Story

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Code

PUPPI Arduino sketch

Arduino
Arduino sketch that implements our dog bark detection Machine Learning Edge Impulse model.
/* Edge Impulse Arduino examples
 * Copyright (c) 2021 EdgeImpulse Inc.
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in
 * all copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */

// If your target is limited in memory remove this macro to save 10K RAM
#define EIDSP_QUANTIZE_FILTERBANK   0

/**
 * Define the number of slices per model window. E.g. a model window of 1000 ms
 * with slices per model window set to 4. Results in a slice size of 250 ms.
 * For more info: https://docs.edgeimpulse.com/docs/continuous-audio-sampling
 */
#define EI_CLASSIFIER_SLICES_PER_MODEL_WINDOW 3

/* Includes ---------------------------------------------------------------- */
#include <PDM.h>
#include <ArduinoBLE.h>
#include <puppi_inference.h>

/** Audio buffers, pointers and selectors */
typedef struct {
    signed short *buffers[2];
    unsigned char buf_select;
    unsigned char buf_ready;
    unsigned int buf_count;
    unsigned int n_samples;
} inference_t;

static inference_t inference;
static bool record_ready = false;
static signed short *sampleBuffer;
static bool debug_nn = false; // Set this to true to see e.g. features generated from the raw signal
static int print_results = -(EI_CLASSIFIER_SLICES_PER_MODEL_WINDOW);

BLEDevice central;
BLEService batteryService("180F");
BLEUnsignedCharCharacteristic batteryLevelChar("2A19", BLERead | BLENotify);

 #define RED 22 
 #define GREEN 23    
 #define BLUE 24

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

    pinMode(RED, OUTPUT);
    pinMode(GREEN, OUTPUT);
    pinMode(BLUE, OUTPUT);

    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]));

    run_classifier_init();
    if (microphone_inference_start(EI_CLASSIFIER_SLICE_SIZE) == 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);
    
    BLE.setLocalName("PredictionMonitor");
    BLE.setAdvertisedService(batteryService);
    batteryService.addCharacteristic(batteryLevelChar);
    BLE.addService(batteryService); // Add the battery service
    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()
{
    bool m = microphone_inference_record();
    if (!m) {
        ei_printf("ERR: Failed to record audio...\n");
        return;
    }

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

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

    int BT_result = 0;

    if (result.classification[2].value < result.classification[0].value
    && result.classification[2].value < result.classification[1].value
    && result.classification[2].value < result.classification[3].value) {
      if (result.classification[0].value > result.classification[1].value
      && result.classification[0].value > result.classification[3].value
      && result.classification[0].value > 0.65) {
        digitalWrite(RED, LOW);
        digitalWrite(GREEN, HIGH);
        digitalWrite(BLUE, HIGH);
        BT_result = 1;
      } else if(result.classification[1].value > result.classification[0].value
      && result.classification[1].value > result.classification[3].value
      && result.classification[1].value > 0.65) {
        digitalWrite(RED, LOW);
        digitalWrite(GREEN, LOW);
        digitalWrite(BLUE, HIGH);
        BT_result = 2;
      } else if(result.classification[3].value > result.classification[0].value
      && result.classification[3].value > result.classification[1].value
      && result.classification[3].value > 0.65) {
        digitalWrite(RED, HIGH);
        digitalWrite(GREEN, HIGH);
        digitalWrite(BLUE, LOW);
        BT_result = 3;
      } else {
      digitalWrite(RED, HIGH);
      digitalWrite(GREEN, HIGH);
      digitalWrite(BLUE, HIGH);
      }
    } else {
      digitalWrite(RED, HIGH);
      digitalWrite(GREEN, HIGH);
      digitalWrite(BLUE, HIGH);
    }

    if (central.connected()) batteryLevelChar.writeValue(BT_result);

    if (++print_results >= (EI_CLASSIFIER_SLICES_PER_MODEL_WINDOW)) {
        // 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 EI_CLASSIFIER_HAS_ANOMALY == 1
        ei_printf("    anomaly score: %.3f\n", result.anomaly);
#endif

        print_results = 0;
    }
}

/**
 * @brief      Printf function uses vsnprintf and output using Arduino Serial
 *
 * @param[in]  format     Variable argument list
 */
void ei_printf(const char *format, ...) {
    static char print_buf[1024] = { 0 };

    va_list args;
    va_start(args, format);
    int r = vsnprintf(print_buf, sizeof(print_buf), format, args);
    va_end(args);

    if (r > 0) {
        Serial.write(print_buf);
    }
}

/**
 * @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 (record_ready == true) {
        for (int i = 0; i<bytesRead>> 1; i++) {
            inference.buffers[inference.buf_select][inference.buf_count++] = sampleBuffer[i];

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

/**
 * @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.buffers[0] = (signed short *)malloc(n_samples * sizeof(signed short));

    if (inference.buffers[0] == NULL) {
        return false;
    }

    inference.buffers[1] = (signed short *)malloc(n_samples * sizeof(signed short));

    if (inference.buffers[0] == NULL) {
        free(inference.buffers[0]);
        return false;
    }

    sampleBuffer = (signed short *)malloc((n_samples >> 1) * sizeof(signed short));

    if (sampleBuffer == NULL) {
        free(inference.buffers[0]);
        free(inference.buffers[1]);
        return false;
    }

    inference.buf_select = 0;
    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);

    // optionally set the gain, defaults to 20
    PDM.setGain(80);

    PDM.setBufferSize((n_samples >> 1) * sizeof(int16_t));

    // 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!");
    }

    record_ready = true;

    return true;
}

/**
 * @brief      Wait on new data
 *
 * @return     True when finished
 */
static bool microphone_inference_record(void)
{
    bool ret = true;

    if (inference.buf_ready == 1) {
        ei_printf(
            "Error sample buffer overrun. Decrease the number of slices per model window "
            "(EI_CLASSIFIER_SLICES_PER_MODEL_WINDOW)\n");
        ret = false;
    }

    while (inference.buf_ready == 0) {
        delay(1);
    }

    inference.buf_ready = 0;

    return ret;
}

/**
 * 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.buffers[inference.buf_select ^ 1][offset], out_ptr, length);

    return 0;
}

/**
 * @brief      Stop PDM and release buffers
 */
static void microphone_inference_end(void)
{
    PDM.end();
    free(inference.buffers[0]);
    free(inference.buffers[1]);
    free(sampleBuffer);
}

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

PUPPI Arduino machine learning library

Arduino
Needs to be included in order for the Arduino detection sketch to work
sketch > include library > add .ZIP library
No preview (download only).

PUPPI Android App

Java
Made with MIT App Inventor
No preview (download only).

Credits

luka pogorelz

luka pogorelz

1 project • 0 followers
Student by day, photographer by night.
Benjamin Tajč

Benjamin Tajč

1 project • 0 followers
Mehdi Elouissi

Mehdi Elouissi

1 project • 0 followers
h

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