Bitte & Danke

  // Global variable to store the classifier

let classifier;

​

// Label

let label = 'listening...';

let confidence;

​

let bitte = 0;

let danke = 0;

​

let img, img_clap, img_snap; //Piktogramme

​

// Teachable Machine model URL:

let soundModel = 'https://teachablemachine.withgoogle.com/models/x_NwwYXkj/'; //Teachable Machine

​

​

function preload() {

  // Load the model

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

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

  img_clap = loadImage("1F44F_black.png"); // bitte

  img_snap = loadImage("1F44C_black.png"); // danke

}

​

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, width/2, height/2);

  //textSize(50);

  //textAlign(CENTER, CENTER);

  //text("Bitte: " + bitte, width / 2, height / 3);

  //text("Danke: " + danke, 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("bitte") > -1 && confidence > 0.9) {

    img = img_clap; //Piktogramm bitte

    print("bitte");

  }

    //bitte += 1;

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

    img = img_snap; //Piktogramm danke

    print("danke");

    //danke += 1;

  }

}