Sound Classification OpenMoji

  // Global variable to store the classifier

let classifier;

​

// Label

let label = 'listening...';

let confidence;

​

let Tastatur = 0;

let Stift = 0;

​

let img, img_tastatur, img_stift;

​

// Teachable Machine model URL:

let soundModel = 'https://teachablemachine.withgoogle.com/models/UJRwGJgh7/';

​

​

function preload() {

  // Load the model

  classifier = ml5.soundClassifier(soundModel + 'model.json');

  img = loadImage("1F3A4_black.png"); // microphone

  img_tastatur = loadImage("Element 1.svg"); // clap

  img_stift = loadImage("Piktogramm_Kulli.png"); // snap

}

​

function setup() {

  createCanvas(windowWidth, windowHeight);

  // Start classifying

  // The sound model will continuously listen to the microphone

  classifier.classify(gotResult);

}

​

function draw() {

  background(200);

  // Draw the label in the canvas

  fill(255);

  imageMode(CENTER);

  image(img, width/2, height/2);

  //textSize(50);

  //textAlign(CENTER, CENTER);

  //text("Clap: " + clap, width / 2, height / 3);

  //text("Snap: " + snap, width / 2, height / 2);

​

}

​

​

// The model recognizing a sound will trigger this event

function gotResult(error, results) {

  if (error) {

    console.error(error);

    return;

  }

  // The results are in an array ordered by confidence.

  // console.log(results[0]);

  label = results[0].label;

  confidence = nf(results[0].confidence, 0, 2);

  //print(label + " / " + confidence);

  if (label.indexOf("Tastatur") > -1 && confidence > 0.9) {

    img = img_tastatur;

    print("Digital");

  }

    //clap += 1;

  else if (label.indexOf("Stift") > -1 && confidence > 0.9) {

    img = img_stift;

    print("Analog");

    //snap += 1;

  }

}