tuner/analyze.js

document.querySelector('#analyze-pitch button').addEventListener('click', startListening);

const calculateCent = (frequency) => {
	return frequency * 2 ** (1/1200) - frequency
}

const getCentDifference = (startingFrequency, newFrequency) => {
	const frequencyDifference = newFrequency - startingFrequency
	const cent = calculateCent(startingFrequency)

	return frequencyDifference/cent 
}

const generateCentsString = (centDifference) => {
	console.log(centDifference);
	const flatSharpChar = (centDifference > 0) ? '♯' : '♭'
	return flatSharpChar + Math.abs(parseInt(centDifference)).toString().padStart(2, '0')
}

function startListening() {
	navigator.mediaDevices.getUserMedia({ audio: true, video: false })
		.then(stream => {
			const audioContext = new (window.AudioContext || window.webkitAudioContext)();
			const analyser = audioContext.createAnalyser();
			const microphone = audioContext.createMediaStreamSource(stream);
			const scriptProcessor = audioContext.createScriptProcessor(2048, 1, 1);

			analyser.smoothingTimeConstant = 0.3;
			analyser.fftSize = 2048;

			microphone.connect(analyser);
			analyser.connect(scriptProcessor);
			scriptProcessor.connect(audioContext.destination);

			scriptProcessor.onaudioprocess = () => {
				const buffer = new Float32Array(analyser.fftSize);
				analyser.getFloatTimeDomainData(buffer);
				const pitch = autoCorrelate(buffer, audioContext.sampleRate);
				if(pitch != -1){
					document.querySelector('#analyze-pitch dd:nth-of-type(1)').textContent = pitch.toFixed(2)
					document.querySelector('#analyze-pitch dd:nth-of-type(2)').innerHTML = generateCentsString(getCentDifference(newTone, pitch))
				}
			};
		})
		.catch(err => {
			console.error('Error accessing microphone: ' + err);
		});
}

function autoCorrelate(buffer, sampleRate) {
	const SIZE = buffer.length;
	const MAX_SAMPLES = Math.floor(SIZE / 2);
	const MIN_SAMPLES = 0;
	const GOOD_ENOUGH_CORRELATION = 0.9;

	let bestOffset = -1;
	let bestCorrelation = 0;
	let rms = 0;
	let foundGoodCorrelation = false;
	let correlations = new Array(MAX_SAMPLES);

	for (let i = 0; i < SIZE; i++) {
		const val = buffer[i];
		rms += val * val;
	}
	rms = Math.sqrt(rms / SIZE);

	if (rms < 0.01) // not enough signal
		return -1;

	let lastCorrelation = 1;
	for (let offset = MIN_SAMPLES; offset < MAX_SAMPLES; offset++) {
		let correlation = 0;

		for (let i = 0; i < MAX_SAMPLES; i++) {
			correlation += Math.abs((buffer[i]) - (buffer[i + offset]));
		}
		correlation = 1 - (correlation / MAX_SAMPLES);
		correlations[offset] = correlation;

		if ((correlation > GOOD_ENOUGH_CORRELATION) && (correlation > lastCorrelation)) {
			foundGoodCorrelation = true;
			if (correlation > bestCorrelation) {
				bestCorrelation = correlation;
				bestOffset = offset;
			}
		} else if (foundGoodCorrelation) {
			const shift = (correlations[bestOffset + 1] - correlations[bestOffset - 1]) / correlations[bestOffset];
			return sampleRate / (bestOffset + (8 * shift));
		}
		lastCorrelation = correlation;
	}
	if (bestCorrelation > 0.01) {
		return sampleRate / bestOffset;
	}
	return -1;
}