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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,148 @@ | ||
| /** | ||
| * taira - simple smoothing of one dimensional arrays | ||
| */ | ||
| class Taira extends Array { | ||
| /** | ||
| * Smoothen 1D-Array using selected algorithm | ||
| * @param {*} options Takes one of the supported algorithms (defaults to AVERAGE) and its parameters | ||
| */ | ||
| smoothen (...options) { | ||
| return Taira.smoothen(this, ...options) | ||
| } | ||
|
|
||
| /** | ||
| * Smoothen 1D-Array using selected algorithm | ||
| * @param {*} array The input data array | ||
| * @param {Taira.ALGORITHMS} algorithm Takes one of the supported algorithms (defaults to AVERAGE) | ||
| * @param {*} options Parameters for the algorithm | ||
| * @returns {Taira} New smooth array | ||
| * @throws Will throw an error if 2*size+1>=array.length for AVERAGE,MEDIAN and GAUSSIAN algorithm | ||
| */ | ||
| static smoothen (array, algorithm, ...options) { | ||
| let [option1, option2, option3, ...other] = options | ||
| array = array || [] | ||
| switch (algorithm || 0) { | ||
| case Taira.ALGORITHMS.MEDIAN: | ||
| return Taira._median(array, option1 || 2, option2 || 1, option3, ...other) | ||
| case Taira.ALGORITHMS.GAUSSIAN: | ||
| return Taira._gaussian(array, option1 || 2, option2 || 2, option3, ...other) | ||
| default: | ||
| return Taira._average(array, option1 || 2, option2 || 1, option3, ...other) | ||
| } | ||
| } | ||
|
|
||
| /** | ||
| * Taira.ALGORITHMS.AVERAGE (do not use directly) | ||
| * @param {*} array The input data array | ||
| * @param {integer} size The number of neighbor elements to take, results in 2*size+1 | ||
| * @param {integer} pass How many times to go over the array | ||
| * @param {boolean} circular Joins beginning and end of array, to make the array circular | ||
| * @returns {*} Array calculated with Taira.ALGORITHMS.AVERAGE | ||
| */ | ||
| static _average (array, size, pass, circular) { | ||
| if (array.length <= 2 * size + 1) throw new Error('Array needs to be longer than the box size (2*size+1).') | ||
| let out = new Taira() | ||
| array.forEach((_, index) => { | ||
| if ((index - size < 0 || index + size >= array.length) && !circular) { | ||
| out.push(array[index]) | ||
| } else { | ||
| let segmentstart = (index - size < 0) ? (index - size) + array.length : index - size | ||
| let sum = 0 | ||
| for (let a = segmentstart; | ||
| (index + size + 1) % array.length !== a; a = a % array.length) { | ||
| sum += array[a] | ||
| a++ | ||
| } | ||
| out.push(sum / ((size * 2) + 1)) | ||
| } | ||
| }) | ||
| if (pass > 1) { | ||
| return Taira._average(array, size, --pass) | ||
| } else { | ||
| return out | ||
| } | ||
| } | ||
|
|
||
| /** | ||
| * Taira.ALGORITHMS.MEDIAN (do not use directly) | ||
| * @param {*} array The input data array | ||
| * @param {integer} size The number of neighbor elements to take, results in 2*size+1 | ||
| * @param {integer} pass How many times to go over the array | ||
| * @param {boolean} circular Joins beginning and end of array, to make the array circular | ||
| * @returns {*} Array calculated with Taira.ALGORITHMS.MEDIAN | ||
| */ | ||
| static _median (array, size, pass, circular) { | ||
| if (array.length <= 2 * size + 1) throw new Error('Array needs to be longer than the box size (2*size+1).') | ||
| let out = new Taira() | ||
| array.forEach((_, index) => { | ||
| if ((index - size < 0 || index + size >= array.length) && !circular) { | ||
| out.push(array[index]) | ||
| } else { | ||
| let segmentstart = (index - size < 0) ? (index - size) + array.length : index - size | ||
| let median = [] | ||
| for (let a = segmentstart; | ||
| (index + size + 1) % array.length !== a; a = a % array.length) { | ||
| median.push(array[a]) | ||
| a++ | ||
| } | ||
| median = median.sort((a, b) => { | ||
| if (a < b) { | ||
| return -1 | ||
| } | ||
| if (a > b) { | ||
| return 1 | ||
| } | ||
| return 0 | ||
| }) | ||
| out.push(median[Math.trunc(((size * 2) + 1) / 2)]) | ||
| } | ||
| }) | ||
| if (pass > 1) { | ||
| return Taira._median(array, size, --pass) | ||
| } else { | ||
| return out | ||
| } | ||
| } | ||
|
|
||
| /** | ||
| * Taira.ALGORITHMS.GAUSSIAN (do not use directly) | ||
| * @param {*} array The input data array | ||
| * @param {integer} kernel Size of the kernel array is e.g. 2*kernel+1 | ||
| * @param {*} radius The blur radius (sigma from the gaussian function) | ||
| * @param {boolean} circular Joins beginning and end of array, to make the array circular | ||
| * @returns {*} Array calculated with Taira.ALGORITHMS.GAUSSIAN | ||
| */ | ||
| static _gaussian (array, kernel, radius, circular) { | ||
| if (array.length <= 2 * kernel + 1) throw new Error('Array needs to be longer than the kernel size (2*size+1).') | ||
| let out = new Taira() | ||
| let filter = new Float64Array(2 * kernel + 1) | ||
| let denominator1 = radius * Math.sqrt(2 * Math.PI) | ||
| let denominator2 = Math.pow(radius, 2) * 2 | ||
| filter = filter.map((_, index) => Math.exp(-(Math.pow(index - kernel, 2)) / denominator2) / denominator1) | ||
| let normalizer = filter.reduce((acc, val) => acc + val) | ||
| let normfilter = filter.map((value) => value / normalizer) | ||
| array.forEach((_, index) => { | ||
| if ((index - kernel < 0 || index + kernel >= array.length) && !circular) { | ||
| out.push(array[index]) | ||
| } else { | ||
| let segmentstart = (index - kernel < 0) ? (index - kernel) + array.length : index - kernel | ||
| let sum = 0 | ||
| let c = 0 | ||
| for (let a = segmentstart; | ||
| (index + kernel + 1) % array.length !== a; a = a % array.length) { | ||
| sum += array[a++] * normfilter[c++] | ||
| } | ||
| out.push(sum) | ||
| } | ||
| }) | ||
| return out | ||
| } | ||
| } | ||
|
|
||
| Taira.ALGORITHMS = { | ||
| AVERAGE: 0, | ||
| MEDIAN: 1, | ||
| GAUSSIAN: 2 | ||
| } | ||
|
|
||
| // module.exports = Taira |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1 +1,3 @@ | ||
| const smoothing = 0; | ||
| // smoothing is applied to the audio file to smooth the signal | ||
| // expected values are from 0 (no smoothing) to 100 (lots of smoothing) | ||
| const smoothing = 25; |