VowelWorm is based off the work done at the Department of Computational Perception at Johannes Kepler Universität Linz, and uses the same name. The goal is to produce real-time vocal analysis in the browser.

Read the docs.

VowelWorm can plot vocalizations against the IPA vowel chart in real time, for multiple audio sources at a time. Videos, audio files, and microphone input all work. Data comes from MFCC extraction. See demo

var graphs_element = document.getElementById("graphs"),
    game = new window.VowelWorm.Game({element: graphs_element});

navigator.getUserMedia({audio: true}, function success(){
  var worm = new window.VowelWorm.instance(stream);
  game.addWorm(worm);
});

In addition to extracting MFCCs, VowelWorm also supports applying a Hanning window or Savitzky-Golay filter to FFT data to attempt to extract formants. Using the draw module, this data can be plotted. See demo

var worm = new VowelWorm.instance(document.getElementById('media')),
    el = worm.draw.create(800, 500, 0xFFFFFF);

worm.draw.drawAxes();
document.getElementById('graphs').appendChild(el);

function draw(){
  worm.draw.drawDataLines();
  window.requestAnimationFrame(draw);
}
window.requestAnimationFrame(draw);

VowelWorm can extract MFCCs from any audio or video source.

var worm = new VowelWorm.instance(audio);
var mfccs = worm.getMFCCs({
  minFreq: 300,
  maxFreq: 8000,
  filterBanks: 20
});

VowelWorm can smooth data using either a Hanning window or a Savitzky-Golay filter.

var data = [...], // or data = new Float32Array(...)
    window_size = 55,
    order = 1;

// NOTE: savitzkyGolay requires numericJS to be loaded
var smooth1 = VowelWorm.savitzkyGolay(data, window_size, order);

smooth2 = VowelWorm.hann(data, window_size); // not all window sizes supported (yet!)

VowelWorm can attempt to extract formants. NOTE: this is very much a work in progress.

var worm = new VowelWorm.instance(audio_source);
var formants = worm.getFormants();

VowelWorm has methods for normalizing data, like the Bark Scale.

var f1 = 300,
    f2 = 950;

var f1_bark = VowelWorm.Normalization.barkScale(f1);
var f2_bark = VowelWorm.Normalization.barkScale(f2);

• VowelWorm base code: src/vowelworm.js
• Library Files: src/lib/
• Modules attached to individual VowelWorm.instance objects: src/modules/worm
• Additions to VowelWorm object: src/modules/core
• Test assets: test/assets

JavaScript files should be annotated using JSDoc and Google Closure Compiler's standards for improved compilation

Many of our algorithms are based off Python scripts. If you would like to test the Hanning Window functionality to compare it to an LPC analysis and an FFT analysis, use the windowing.py script, coupled with WaveSurfer export files, as such

$ python windowing.py --fft=./path/to/fft-spectrum.txt --lpc=./path/to/lpc-spectrum.txt

Sample data is used if python windowing.py is called without both the FFT and LPC arguments.