yin.js

;(function(root, factory) {
    if (typeof define === 'function' && define.amd) {
        define(function() {
            return (root.yin = factory())
        })
    } else if (typeof module === 'object' && module.exports) {
        module.exports = factory()
    } else {
        root.yin = factory()
    }
})(typeof self !== 'undefined' ? self : this, function() {
    var DEFAULT_THRESHOLD = 0.07

    var floor = Math.floor

    function difference(data) {
        var n = data.length
        var results = new Float32Array(n)
        var difference
        var summation
        for (var tau = 0, windowSize = floor(n * 0.5); tau <= windowSize; tau++) {
            summation = 0
            for (var j = 0; j < windowSize; j++) {
                difference = data[j] - data[j + tau]
                summation += difference * difference
            }
            results[tau] = summation
        }
        return results
    }

    function cumulativeMeanNormalizedDifference(data) {
        var n = data.length
        var results = new Float32Array(n)
        var summation

        for (var tau = 0; tau < n; tau++) {
            summation = 0
            for (var j = 0; j <= tau; j++) {
                summation += data[j]
            }
            results[tau] = data[tau] / (summation / tau)
        }
        return results
    }

    function absoluteThreshold(data, threshold) {
        var x
        var k = Number.POSITIVE_INFINITY
        var tau

        for (var i = 0, n = data.length; i < n; i++) {
            x = data[i]
            if (x < threshold) {
                return i
            }
            if (x < k) {
                k = x
                tau = i
            }
        }
        return tau
    }

    function bestLocalEstimate(data, tau) {
        var i = tau + 1
        var n = data.length
        var k = data[tau]
        while (i < n && data[i] < k) {
            k = data[i]
            i++
        }
        return i - 1
    }

    /**
     * Estimates fundamental frequency of an audio signal given its time-domain data
     *
     * @param {Float32Array} data The time-domain data for the audio signal
     * @param {Number} sampleRate The sample rate
     * @param {Number} [threshold = 0.07] The threshold
     * @returns {Number} frequency
     */
    return function(data, sampleRate, aThreshold) {
        var threshold = aThreshold || DEFAULT_THRESHOLD
        var results = cumulativeMeanNormalizedDifference(difference(data))
        var tau = absoluteThreshold(results, threshold)
        return sampleRate / bestLocalEstimate(results, tau)
    }
})