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<!DOCTYPE html>
<html lang="en">
<!-- ======= Head Section - DON'T CHANGE ======= -->
<head>
<meta charset="utf-8">
<meta content="width=device-width, initial-scale=1.0" name="viewport">
<title>Electroglottography</title>
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<h1 style = "font-size: 36px">An efficient phonation-driven control system using laryngeal bioimpedance and machine learning</h1>
<!-- <h2><i>Eugenio Donati <sup>1</sup>, Christos Chousidis <sup>1</sup>, Massoud Zolgharni <sup>1</sup><i></h2>
<h3>
<sup>1</sup>School of Computing and Engineering, University of West London, London, United Kingdom <h3> -->
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</section><!-- End Title -->
<!-- Main Webpage Section -->
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<!-- ======= Project Body Section ======= -->
<section id="project-body">
<!-- ======= Project Introduction ======= -->
<div class="project-intro">
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<div class="col-12">
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<hr>
<h1><b>Project Introduction</b> </h1>
<br>
<h3> Laryngeal bioimpedance can deliver unique information about human
voice and phonation. This project is comprised of two aspects: the development of a
self-calibrating laryngeal bioimpedance measurement system and its application in voice
features extraction. The implementation of Artificial Neural Networks is focused on the
near real-time classification of
voice acts in the distinction between speech and singing. One of the main contributions
of this project is represented by the creation of a unique dataset of laryngeal bioimpedance
measurements for the training of Artificial Neural Networks. The development of a self-calibrating
measurement system, alongside the derived dataset, aims to make the technology more employed in voice
disorder evaluation and pre-diagnosis as well as broadening the spectrum of possible applications.<br><br>
</h3>
</div>
</div>
</div>
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<div align="center">
<img class="img-fluid" src="assets\img\projects\Electroglottography\Fig1.png" width = "100% " height=" 100%" alt="IMAGE Dataset">
</div>
</div>
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<div align="center">
<img class="img-fluid" src="assets\img\projects\Electroglottography\EGG2.PNG" width = "70% " height=" 200 px" alt="IMAGE Dataset">
</div>
</div>
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<!-- <div class="container">
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<img src = "assets\img\projects\Electroglottography\EGG signal.jpg" width = "70% " height=" 250 px" >
</td>
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<img src = "assets\img\projects\Electroglottography\EGG2.PNG" width = "70%" height=" 250 px" >
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</div>
</div>
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<div align="justify">
<h2><b>Dataset</b></h2> <br>
<h3>A laryngeal bioimpedance measurement system developed by the team was used to record voice acts from different users. <br>
The dataset is comprised of two sections: <br>singing voices and speaking voices.</h3>
</div>
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</div>
</div>
</div>
<!-- ======= Project Architecture ======= -->
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<h2><b>Network Architecture</b></h2>
</div>
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<div align="justify">
<h3> <br>For the processing of the data, we employed the use of Mel Frequency
Cepstrum Coefficients (MFFC). This delivers a series of frequency coefficients
over 20 frequency bands. The resulting array is the data being fed to the network.
<br><br>The network is therefore comprised as follows:<br><br>
Input layer: has 20 Neurons<br><br>
1 Hidden layer: has 40 Neurons<br><br>
Output Layer: has 1 Neuron for binary classification
<br><br>
<h3>The figure on the right shows the network architecture.</h3>
</h3> <br> <br>
<h2><b>Implementation</b></h2> <br><br>
<h3>The models were implemented in Python within the TensorFlow 2.0 framework.</h3><br> <br>
</div>
</div>
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<img class="img-fluid" src="assets\img\projects\Electroglottography\Net.png" alt="Flowchart">
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</div>
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<h2><b>Network Architecture</b></h2>
</div>
<div class="col-12 col-md-12 col-lg-6 col-xl-6">
<h3><br>For the processing of the EGG data, we employed the use of Mel Frequency Cepstrum Coefficients (MFFC).
This delivers a series of frequency coefficients over 20 frequency bands.
The resulting array is the data being fed to the network.<br><br>
<b> The network is therefore comprised as follows: </b><br><br>
Input layer -> 20 Neurons<br><br>
2 Hidden layers -> 5 Neurons<br><br>
Output Layer -> 1 Neuron for binary classification<br><br>
</h3>
</div>
<! <div class="col-12 col-md-12 col-lg-6 col-xl-6">
<br>
<img class="img-fluid" src="" alt="IMAGE PLACEHOLDER">
</div> -->
<!-- </div>
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<!-- ======= Project Implementation ======= -->
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<hr>
<h2><b>Implementation</b></h2> <br>
<h3>The models were implemented in Python within the TensorFlow 2.0 framework.</h3><br> <br>
<h3>The following figure shows the general flowchart for the implemntation process for EGG to voice converter.</h3>
</div>
</div>
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<div align = "center"
<img class="img-fluid" src="assets\img\projects\Electroglottography\Flowchart1.gif" alt="IMAGE PLACEHOLDER">
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<h2><b>Performance assessment</b></h2>
</div>
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<img class="img-fluid" src="assets\img\projects\Electroglottography\Assessment1.png" alt="IMAGE PLACEHOLDER">
<div class="caption" >Accuracy comparison by the number of neurons for the hidden layer</div>
</div>
</div>
</div>
<div class="col-12 col-lg-6 col-md-6 text-center">
<img class="img-fluid" src="assets\img\projects\Electroglottography\Assessment2.png" alt="IMAGE PLACEHOLDER">
<div class="caption" >Model accuracy for 40 hidden neurons </div>
</div>
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<div align="justify">
<img class="img-fluid" src="assets\img\projects\Electroglottography\Assessment3.png" alt="IMAGE PLACEHOLDER">
<div class="caption" >Confusion matrix for “unseen” data </div>
</div>
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</div>
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<!-- ======= Project Results ======= -->
<!-- <div class="project-results">
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<h2><b>Results</b></h2>
<p>
</div>
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<div class="col-12 col-lg-6">
<div align="justify">
<h3><br>The experiments presented in this chapter showed that EGG can be successfully used for an efficient and
accurate conversion of voice-to-MIDI in a near real-time environment. <br>
The main limitation showed by the system is represented by the latency between a phonation act and the delivery
of the MIDI messages. The performance assessment shows a latency of ∼20 ms that, despite acceptable in perceptual
terms, would require for optimal real-time operation to be reduced to 10-15 ms</h3>
</div>
</div>
<div class="col-12 col-lg-6 text-center">
<img class="img-fluid" src="assets\img\projects\Electroglottography\results.jpg" alt="IMAGE PLACEHOLDER">
</div>
</div>
</div> -->
<div class="container">
<hr>
<div class="row justify-content-left">
<div class="col-12">
<div class="text-left">
<h1> <b> Project Team <b></h1> <br>
<h6> <a href="https://twitter.com/intsav_?lang=en-gb" target="_blank" rel="noopener noreferrer"> Eugenio Donati</a></h6>
<h6> <a href="https://www.surrey.ac.uk/people/christos-chousidis" target="_blank" rel="noopener noreferrer">Christos Chousidis </a></h6>
<h6> <a href="https://www.uwl.ac.uk/staff/massoud-zolgharni" target="_blank" rel="noopener noreferrer"> Massoud Zolgharni </a></h6>
</div>
</div>
</div>
</div>
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<hr>
<div class="row justify-content-left">
<div class="col-12">
<div class="text-left">
<h1> <b> References </b></h1>
<h3> <a href="https://www.sciencedirect.com/science/article/pii/S2772528622000036" target="_blank" rel="noopener noreferrer"> Development of a novel Electroglottography sensors system, using advanced electronics design and deep learning</a></h3>
<h3> <a href="https://ieeexplore.ieee.org/document/9856413 " target="_blank" rel="noopener noreferrer"> Electroglottography based voice-to-MIDI real time converter with AI voice act classification
</a></h3>
</div>
</div>
</div>
</div>
</div>
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<br><br>
<a class="btn btn-primary" href="https://www.sciencedirect.com/science/article/pii/S2772528622000036" role="button">Download the paper</a>
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