formantanalyzer.js

!npm
!GitHub branch checks state
!NPM

A JS Web API based spectrum analyzer for speech and music analysis. It can be used for labeling or feature extraction.

!Preview

Demo

Bare minimum starter file: tabahi.github.io/formantanalyzer.js

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- Web Speech Analyzer
- Speech Emotion Analyzer

Installation

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Load the javascript module as
``html

`
Then use the entry point FormantAnalyzer to use the imported javascript library.

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Prerequisites: Node.js, check versions >12 for node -v and >6 for npm -v.

- For importing the JS package in webpack project: Install formantanalyzer using npm

`cmd
npm i formantanalyzer
:: or
npm install formantanalyzer --save-dev
`


`javascript
const FormantAnalyzer = require('formantanalyzer'); //after npm installation
FormantAnalyzer.configure(launch_config);
FormantAnalyzer.LaunchAudioNodes(2, webAudioElement, call_backed_function, ['file_label'], false, false);
`

If you are creating a webpack project from scratch by just using JS files from ./src then first initialize a new webpack project by npx webpack and create a new webconfig.js in project root directory. See instructions here.

Usage

First, configure the fomant analyzer. Pass the #SpectrumCanvas element if plot is enabled. Pass null if no need for plot. See 'index.html' for a simple example.

HTML:
`html



`
In javascript:

`javascript
/*Using
Can also import in webpack as:
const FormantAnalyzer = require('formantanalyzer');
*/

function Configure_FormantAnalyzer()
{
const BOX_HEIGHT = 300;
const BOX_WIDTH = window.screen.availWidth - 50;
document.getElementById('SpectrumCanvas').width = BOX_WIDTH; //reset the size of canvas element
document.getElementById('SpectrumCanvas').height = BOX_HEIGHT;

let launch_config = { plot_enable: true,
spec_type: 1, //see below
output_level: 2, //see below
plot_len: 200, f_min: 50, f_max: 4000,
N_fft_bins: 256,
N_mel_bins: 128,
window_width: 25, window_step: 15,
pause_length: 200, min_seg_length: 50,
auto_noise_gate: true, voiced_min_dB: 10, voiced_max_dB: 100,
plot_lag: 1, pre_norm_gain: 1000, high_f_emph: 0.0,
plot_canvas: document.querySelector('#SpectrumCanvas').getContext('2d'),
canvas_width: BOX_WIDTH,
canvas_height: BOX_HEIGHT };

FormantAnalyzer.configure(launch_config);
}
`


Initialize an Audio Element, or local audio file binary element, or null if using the mic stream. Then pass it to LaunchAudioNodes with suitable parameters. See 'index.html' for examples for local audio file and mic streaming.

`javascript

var webAudioElement = new Audio("./audio_file.mp3");
/Parameters:/
const context_source = 2; //1: Local file binary, 2: play from a web Audio, 3: mic
const test_mode = true; //plots only, it does not return callback
const offline = false; //play on speakers, set true to play silently
const file_labels =[]; //array of labels that will be passed to callback after feature extraction

/ Wait for audio file to load /
webAudioElement.addEventListener("canplaythrough", event => {
/ Launch Audio Nodes /
FormantAnalyzer.LaunchAudioNodes(context_source, webAudioElement,
callback, file_labels, offline, test_mode).then(function()
{
console.log("Done");
}).catch((err)=>{
console.log(err);
});
});

callback(seg_index, file_labels, seg_time, features)
{
//callback function to which extracted features are passed
}
`

LaunchAudioNodes()

Returns: This function returns a promise as resolve(true) after playback is finished or reject(err) if there is an error.
If you want an abrupt stop, then call the FormantAnalyzer.stop_playing("no reason") function. Then this function will return resolve("no reason"). Different audio contexts are buffered/streamed differently, therefore each has a separate function in AudioNodes.js.

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context_source (int):
- 1 --- Play from a locally loaded file (pass an audio binary as source_obj).
- 2 --- Play from an Audio element (pass an Audio object as source_obj)
- 3 --- Stream from mic

source_obj (object):
Source audio object.
- If context_source==1 (playing from a local file) then pass a binary of file. Get binary from FileReader as: FileReader.onload (e)=>(binary = e.target.result). See index.html.
- If context_source==2 (playing from a web address) then pass an Audio object.
- If context_source==3 (playing from mic Pass null). See index.html.

callback:
It is the callback function to be called after each segment ends. It should accept 4 variables; segment_index, segment_time_array, segment_labels_array, segment_features_array. Callback is called asynchronously, so there might be a latency between audio play and it's respective callback, that's why it's important to send the labels to async segmentor function.

file_labels (Array):
It is an array of labels for currently playing file. It is returned as it is to the callback function.
It is used to avoid the label mismatch during slow async processing in case if a new file is playing, but the callback sends the output of the previous one.
Sometimes callback is called with a delay of 2 seconds, so it helps to keep track which file was playing 2 seconds ago.

offline_mode (boolean):
If true then the locally loaded files will be played silently in an offline buffer.

test_play (boolean)
set it true to avoid calling the callback. Plots and AudioNodes will still work as it is, but there will be no call backs. It can be enabled to test plotting or re listening.

play_offset and play_duration are in seconds to play a certain part of the file, otherwise pass null.

As an example, callback function in WebSpeechAnalyzer app looks like this:

`javascript

async function callback(seg_index, seg_label, seg_time, features)
{
if(launch_config.output_level == 13) //Syllable 53x statistical features for each segment
{
if(settings.collect)
for(let segment = 0; segment < features.length; segment++ )
{
storage_mod.StoreFeatures(launch_config.output_level, settings.DB_ID, (seg_index + (segment/100)), seg_label, seg_time[segment], features[segment]);
}

if(settings.plot_enable && settings.predict_en)
{
pred_mod.predict_by_multiple_syllables(settings.predict_type, settings.predict_label, seg_index, features, seg_time);
}
}
}

`

Different types of features are returned to the callback(seg_index, seg_label, seg_time, features) at different output levels. Variables for callback:
- seg_index the index of segment since the play start. Segments are separated by significant pauses, the first segment of each file starts has seg_index = 0.
- seg_label is the same as file_labels passed to LaunchAudioNodes().
- seg_time is an array of two elements [start_time, total_duration] at segment level. At syllable level, it is a 2D array of shape (syllables, 2), giving [start_time, total_duration] for each syllable separately.
- features is the array of extracted features that is of different shape at different output_level. Detailed descriptions for each level are given below.

configure()

Before playing any audio source using LaunchAudioNodes(), the audio nodes must be configured otherwise, default launch_config settings will be assumed.

`javascript
let launch_config = { plot_enable: true,
spec_type: 1,
output_level: 4,
plot_len: 200,
f_min: 50,
f_max: 4000,
N_fft_bins: 256,
N_mel_bins: 128,
window_width: 25,
window_step: 15,
pause_length: 200,
min_seg_length: 50,
auto_noise_gate: true,
voiced_min_dB: 10,
voiced_max_dB: 100,
plot_lag: 1,
pre_norm_gain: 1000,
high_f_emph: 0.0,
plot_canvas: document.querySelector('#SpectrumCanvas').getContext('2d'),
canvas_width: 900,
canvas_height: 300 };

FormantAnalyzer.configure(launch_config);
`

Available spec_type options:
- 1 = Mel-spectrum
- 2 = Power Spectrum
- 3 = Discrete FFT

Available output_level options:

- 1 = Bars (no spectrum, only the last filter bank)
- 2 = Spectrum
- 3 = Segments
- 4 = Segment Formants / segment
- 5 = Segment Features 53x / segment [ML]
- 10 = Syllable Formants / syllable
- 11 = Distributions 264x / file [ML]
- 12 = Syllable Curves 23x / syllable [ML]
- 13 = Syllable Features 53x / syllable [ML]

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Different features are returned to the callback(seg_index, seg_label, seg_time, features) at different output levels. The shape of seg_time also differs from (2) to (syllable, 2) for segment vs syllable. The shape of features array at each level is as follows:

- Bar and Spectrum levels return a 1D array of raw FFT or Mel bins (depending on the spec_type) at the interval of each window step (~25 ms). The only difference is that Spectrum level keeps a history of bins for plotting the spectrum. Spectrum level returns callback with a 2D array of size plot_len x N_bins after each min_seg_length milliseconds. Bar returns an array of 1 x N_binsof the latest step.

- Segments level returns a 2D array of shape (step, bins) of FFT or Mel-bins for each segment. The 1st axis is along the window steps, 2nd axis is along the FFT or Mel bins at each step. Each segment is separated by pauses in speech.
- Segment Formants returns an array of shape (steps, 9). The 9 features include frequency, energy and bandwidth of 3 most prominent formants at that particular window step. Indices [0,1,2] are the frequency, energy and bandwidth of the lowest frequency formant.
- Syllable Formants returns the same array of shape (steps, 9) as Segment Formants but in this case the division and the length (total number of steps) is much shorter because syllables are separated by even minor pauses and other sudden shifts in formant frequency and energy.
- Segment Features 53x returns a 1D array of shape (53) which has 53 statistical formant based features.
- Syllable Features 53x returns a 2D array of shape (syllables, 53) which has 53 statistical formant based features for each syllable in the segment.
- Syllable Curves 23x returns a 2D array of shape (syllables, 23). The 23 features are the polynomial constants extracted by curve fitting of sum of energies of all formants and curves for f0, f1, f2 frequencies. The fitted curve of energy is visible on the plot but the scale is not Hz.
- Distributions 264x returns a 1D array of shape (264) for normalized cumulative features for complete file since the play started. The sum of features resets only when a new file starts, but the latest normalized feature set is updated at each segment pause using the features of each segment.

Levels 5,11,12,13 have fixed output vector sizes (either per segment, per file, or per syllable) that's why they can be used as input for an ML classifier. At level 11,13, the plot is the same as level 10, but the different types of extracted features are returned to the callback function.

auto_noise_gate: true automatically sets the speech to silence thresholds to detect voiced segments. To use manual thresholds, set it to false and set manual values for voiced_min_dB and voiced_max_dB`.