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Elsevier

Neuroscience Informatics

Volume 2, Issue 2, June 2022, 100041
Neuroscience Informatics

Original article
Electroglottography based real-time voice-to-MIDI controller

Author links open overlay panelEugenioDonati^1ChristosChousidis
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https://doi.org/10.1016/j.neuri.2022.100041Get rights and content
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Abstract

Voice-to-MIDI real-time conversion is a challenging problem that
comes with a series of obstacles and complications. The main issue is
the tracking of the human voice pitch. Extracting the voice
fundamental frequency can be inaccurate and highly computationally
exacting due to the spectral complexity of voice signals. In
addition, on account of microphone usage, the presence of
environmental noise can further affect voice processing. An analysis
of the current research and status of the market shows a plethora of
voice-to-MIDI implementations revolving around the processing of
audio signals deriving from microphones. This paper addresses the
above-mentioned issues by implementing a novel experimental method
where electroglottography is employed instead of microphones as a
source for pitch-tracking. In the proposed system, the signal is
processed and converted through an embedded hardware device. The use
of electroglottography improves both the accuracy of pitch evaluation
and the ease of voice information processing; firstly, it provides a
direct measurement of the vocal folds' activity and, secondly, it
bypasses the interferences caused by external sound sources. This
allows the extraction of a simpler and cleaner signal that yields a
more effective evaluation of the fundamental frequency during
phonation. The proposed method delivers a faster and less
computationally demanding conversion thus in turn, allowing for an
efficacious real-time voice-to-MIDI conversion.

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Keywords

Electroglottography
Bioimpedance measurements
EGG-to-MIDI
Voice-to-MIDI
Voice information retrieval
Real-time audio conversion
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Cited by (0)

Eugenio Donati received his BA (2013) from the UNINT University of
Rome in Interpreting and Translation. He obtained his diploma in
Sonic Arts (2013) from the Saint Louis College of Music of Rome. He
received his BSc (Hons) (2017) in Applied Sound Engineering at the
University of West London (UWL) where he also received the MSc (2018)
in Digital Audio Engineering. Eugenio started his PhD in 2019 at UWL;
his research focuses on Audio Electronics, Biomedical Acoustics and
Machine Learning. Eugenio is part of the Biomedical Acoustics special
interest group within the UK-Acoustics Network (UKAN) and its Early
Careers coordinator.

Christos Chousidis received his B.Eng. from the Technological
Institute of Crete in 1995. In 2006 he received his MPhil and in 2014
his PhD both from Brunel University. His research focusses on
Biomedical Acoustics and Wireless Audio Networks. He is a member of
IEEE and Audio Engineering Society (AES). Christos is also a member
of the Technical Committees on Network Audio Systems (TC-NAS) and
Machine Learning and Artificial Intelligence (TC-MLAI) within the
AES. He is also a founding member of the UKAN's special Interest
Group on Biomedical Acoustics.

^1

    The author conducted the research as part of a PhD at the
    University of West London under the Vice Chancellor Scholarship
    Scheme.

(c) 2022 The Author(s). Published by Elsevier Masson SAS.

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