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

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);
                document.querySelector('#analyze-pitch dd').textContent = pitch.toFixed(2);
            };
        })
        .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;
}