Research in the Department of Music Technology and Acoustics of the

Hellenic Mediterranean University: An Overview and Prospects

SPYROS BREZAS*, STELLA PASCHALIDOU, CHRISOULA ALEXANDRAKI,

MAKIS BAKAREZOS, CHRISTINE GEORGATOU, KONSTANTINOS KALERIS,

MAXIMOS KALIAKATSOS-PAPAKOSTAS, EMMANOUIL KANIOLAKIS-KALOUDIS,

EVAGGELOS KASELOURIS, YANNIS ORPHANOS, HELEN PAPADAKI,

NEKTARIOS A. PAPADOGIANNIS, KATERINA TZEDAKI, NIKOLAS VALSAMAKIS,

VASILIS DIMITRIOU

1Department of Music Technology and Acoustics,

Hellenic Mediterranean University,

E. Daskalaki, Perivolia, 71433 Rethymnon,

GREECE

Abstract: - The Department of Music Technology and Acoustics of the Hellenic Mediterranean University

offers a unique higher education program in Greece, addressing the growing demand for specialists in music

technology, sound technology, and acoustics. It aims to educate specialized professionals in the rapidly

advancing scientific fields of music technology and acoustics, mainly driven by the swift progress in electronic

technology. The Department aims to address a gap in the professional market by producing highly skilled

graduates, capable not only of keeping up with the latest scientific and technological developments but also of

leading the way by introducing innovative approaches and methods. The Department combines art, science, and

technology, focusing on sound recording, analysis, synthesis, and music production. Music technology

encompasses various cutting-edge fields such as network music performance, artificial intelligence in music,

and music embodiment. Acoustics refers to fundamental aspects of sound as well as its generation,

transmission, and related phenomena. It includes research fields such as physical acoustics, optoacoustics, and

vibroacoustics. This overview presents the research activities, methodologies, and results. A discussion of

future research works and pointers to future technological evolution towards real-world music and acoustics

applications is also provided.

Key-Words: - music technology, acoustics, research activities, network music, artificial intelligence, acoustic

ecology, optoacoustics, musical acoustics.

Received: February 21, 2023. Revised: November 16, 2023. Accepted: December 17, 2023. Published: March 20, 2024.

1 Introduction

The Department of Music Technology and

Acoustics (MTA) of the Hellenic Mediterranean

University (HMU), [1], is unique in higher

education in Greece and fulfills the ever-growing

demands for specialized engineers in the fields of

music technology, sound technology, and acoustics.

At an international level, it is among the few BSc

programs with such an interdisciplinary orientation

in their studies, along with the BSc program of the

University of Edinburg entitled “Acoustics and

Music Technology”, [2] and that of the University

of Southampton, [3], entitled “Acoustics with

Music”. Greece lacks specialized human resources

in Music Technology and Acoustics nowadays the

evolution in electronic technology drives a

continuous progress and ever-growing professional

prospects in the field. The Department bridges this

gap by training highly skilled graduates proficient in

a wide variety of related fields. These individuals

are not just adept at keeping up with the latest

advancements in these fields but are also at the

forefront, pioneering innovative approaches and

techniques. The Department has a strong research

orientation and extroversion, through which students

are directly exposed to the developments in the field

at the national and international level, and the

development and establishment of research projects

and cooperation programs with Greek and foreign

universities and stakeholders.

The art of music and sound is strongly

interconnected with science (mathematics, physics)

and technology (informatics, electronics). Music

Technology, [4], [5], focuses on the study and the

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design of mechanisms, algorithms, tools, software

applications, devices, electronic devices, and above

all their synergy as used by musicians to create,

perform, compose, notate, analyze, record and

process music and sound. Modern cutting-edge

fields (state-of-the-art) are Network Music

Performance, Music Information Retrieval,

Artificial Intelligence in Music, Machine Learning

in Audio and Music, Computational Musicology,

Electroacoustic Music, Acoustic Ecology and

Soundscape Ecology, Human Movement Sciences

and Technologies in Music, Gesture-Controlled

Interactive Audio and Music Systems and New

Musical Instruments. The science and technology of

Acoustics, [6], [7], studies the properties and

behavior of sound, as well as its applications.

Acoustics is the science of sound, including its

production, transmission, and effects, including

biological, physiological, and perceptual effects.

The study of acoustics revolves around the

generation, propagation, and reception of

mechanical waves and vibrations. The basic

subfields of Acoustics, [8] are: Physical Acoustics,

Bioacoustics, Engineering Acoustics, Architectural

Acoustics, Environmental noise, Musical Acoustics,

Psychoacoustics, Optoacoustics, Room Acoustics,

Vibroacoustics, and Ultrasonics.

In this work, an overview of the research in the

Department is presented. The research activities,

along with the scientific methodology applied at

each research subfield, are described in section 2,

and representative scientific results are presented in

section 3. This review of our research areas and

their subfields and methodologies aims to

summarize our research studies and motivate new

and innovative research works and pointers for

future potential enhancement of research

achievements on a real-world application level,

considering the strengthening of within-department

and international collaborations.

2 Research Activities and

Methodology

The scientific methods developed and applied in the

research fields of music and sound technology and

of acoustics, are here presented categorized in the

form of research subfields.

2.1 Music and Sound Technology

The main research subfields of music and sound

technology where the MTA significantly contributes

and exhibits leading expertise are music

embodiment, network music performance, artificial

intelligence in music, electroacoustic music

composition, and acoustic ecology and soundscape

ecology. These basic subfields are further presented

and analyzed along with the methods applied.

2.1.1 Music Embodiment

Traditionally, music has been studied through its

symbolic representation, i.e., music notation,

standing for the aesthetic value of a musical work as

a composition. Later, more emphasis was given to

its performative aspect, that is the expressive

rendition of a musical piece by a performer, studied

through the analysis of the musical signal, that is an

audio recording. In recent years we have been

observing a shift of interest with a growing number

of publications emphasizing the role of the human

body and its movement in music. The reason for this

shift lies in theories of embodied cognition—more

recently including embodied music cognition too,

[9], —that consider knowledge as an emergent

phenomenon that is acquired through the action of

doing and dispute the notion that cognition involves

representations, [10], [11]. Since multimodality has

been identified as a central quality of musical

experience and embodiment, [12], [13], empirical

studies of human body movement in music most

often involve capturing a plethora of multimodal

data by movement-related sensors and state-of-the-

art motion capture technologies, previously mostly

employed in the realm of cinema for animation

purposes.

Embodied music cognition is a term that

incorporates all music-related human body

movements, ranging from music performance to

music listening. Examples of experimental work

include, for instance, the study of effort in

Hindustani vocal improvisation on the occasions of

manual interactions with imaginary objects by

singers, [14]; the analysis and rendering of

multimodal data, especially so electromyography

data for analyzing muscle contraction as a measure

of bodily tension, performability and athleticism in

comparison to the musical tension of a composition,

[15]; the rhythmic and stylistic regional diversity of

musician-dancer interaction in traditional Cretan

dances [16]; or even the study of single drumming

strokes as loading conditions to FEM-BEM

mathematical models in cymbal vibroacoustic

behavior, [17].

For instance, [14], reports on the first

ecologically valid study of effort-related mappings

between sound sculpting gestures (gestural

interactions with imaginary objects) and the voice in

Hindustani Dhrupad vocal improvisation. The aim

was to devise formalized descriptions to infer the

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amount of effort that such interactions are perceived

to require and classify gestures as interactions with

elastic versus rigid objects. The findings were

obtained through the application of an empirical

sequential mixed methodology in analyzing original

multimodal data of Dhrupad vocal improvisation

performances.

The data was collected for the specific study,

primarily in India, and includes interviews, audio-

visual material, and 3D movement data captured by

a passive marker-based optical motion capturing

system (Naturalpoint Optitrack). Seventeen

vocalists, encompassing both professionals and

amateurs, with diverse genders, ages, levels of

musical experience, and training durations, were

enlisted for the study, only two of whom were of

non-Indian origin. To ensure uniformity in gestural

resemblance, all selected individuals share the same

musical lineage as disciples of the esteemed vocalist

Zia Fariduddin Dagar, who was also recorded as

part of the study.

Participants were briefed solely on the study's

connection to music and movement, with no

additional details provided. Musicians received

compensation for the recording of performances at

an hourly rate, excluding interviews. Written

informed consent forms and recording agreements

release forms were signed by all participants. To

enhance ecological validity, musicians were

instructed to engage in improvisation without any

specific guidelines. All recordings took place in

domestic settings, typically in the living rooms

where musicians conduct their daily musical

activities, adapted to accommodate recording needs

as depicted in Figure 1.

Fig. 1: Typical equipment setup, photo taken at the

music school of maestro Zia Fariddudin Dagar in

Palaspe, Panvel in India, [14]

The methodology considered both first-person

and third-person perspectives in the analysis of

musical gestures, [18] and involved the integration

of qualitative ethnographic techniques (thematic

analysis of interviews and video observation

analysis) with quantitative methods (regression

analysis for effort inference and gesture

classification based on a combination of acoustic

and movement features). Figure 2 illustrates the

sequential mixed methodology that guided the

study, featuring gesture images sourced from [19].

The findings of this research will be detailed in the

Results Section 3.1.1.

The motivation for such systematic work is

twofold: On the one hand, to gain a deeper

understanding of various music traditions and

conditions, and on the other hand, to acquire

knowledge of how gesture-sound links deduced

from designed experiments could lead to human-

computer-interactions for music that are more

physically plausible, [18], [20]. Hence, the results of

such work may have implications for strategies for

the development of artificial gesture-sound

mappings in the design of electronic musical

instruments and interactive music and music-related

applications, [19], [21], [22].

Fig. 2: Sequential mixed methodology featuring

gesture images, [14]

2.1.2 Network Music Performance

Considering the performative aspect of music, the

last few decades have witnessed a significant body

of research efforts dedicated to enabling musicians

to collaborate in virtual environments, thus

circumventing the necessity for physical co-

presence. The relevant research area is known as

Networked Music Performance (NMP) and is

particularly challenging when considering true-

bidirectional audio-visual interactions of musicians

over computer networks, [23]. Compared to

common teleconferencing systems, NMP systems

have multiple requirements, which mainly account

for reducing communication latencies and

increasing the quality of live audio streams. Speech-

based human interaction, in teleconferencing and

VoIP applications, is highly tolerant to latency, with

an acceptable mouth-to-ear delay of 150-200 ms,

[24]. Unfortunately, in music performance, the

tolerable communication latency is lower by

approximately an order of magnitude, i.e. between

25-30 ms and it is known as Ensemble Performance

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Threshold (EPT), or “the level of delay at which

effective real-time musical collaboration shifts from

possible to impossible”, [25].

Further to latency, NMP systems are

characterized by excessive requirements in network

throughput. This is mainly because, unlike speech,

most musical instruments have a broad acoustic

spectrum, hence requiring high sampling rates (at

least 44.1 kHz, as opposed to speech signals that

commonly use 8 kHz). Moreover, due to the

excessive requirements in reducing latency and

increasing throughput, live audio signals are highly

prone to sound distortions owing to data losses over

the network. In Wide Area Networks (WAN),

packet loss is frequently observed and caused by

congested network paths or by faulty networking

hardware across the path. In the case of audio,

losing network packets will result in dropouts at the

receiving end. Audio dropouts correspond to signal

discontinuities perceived as glitches, which, in the

case of NMP, can seriously hinder the collaboration

of music performers.

Despite almost three decades of research efforts,

the main challenges faced by the implementation of

NMP systems have not been defeated. NMP is a

vision rather than a technological affordance and

may only be feasible under certain conditions,

namely through highly reliable network

infrastructures (e.g. academic networks) and short

geographical distances. Nevertheless, the ongoing

progress in network and audio codec technology,

e.g. the Opus codec, which is currently the de facto

standard for real-time audio streaming over IP,

allows us to increasingly consider alternative setups

in which NMP may be feasible as well as useful,

[23].

Moreover, recent progress in computational

intelligence and machine musicianship suggests the

advancement of NMP applications equipped with

predictive capabilities to anticipate and reproduce

musicians’ performance remotely, thereby

mitigating latency and quality constraints. At

present, NMP research is highly interdisciplinary as

it involves numerous aesthetic, technical, and

perceptual aspects. Among other domains involving

networked collaboration, the COVID-19 pandemic

manifested a compelling need for the development

of systems supporting online music education. In

music learning and teaching, it is rather uncommon

for connected peers to simultaneously perform the

same piece of music. It is instead more common that

a teacher or a student will perform a musical

excerpt, and others will be required to imitate or

discuss the performance. This practice allows for

alleviating the excessive requirements in network

reliability and realizing more feasible NMP setups.

Researchers appear to be increasingly investing in

the development of novel applications and services

for online music learning, [26]. The innovation

capacity as well as the improvement of user

experience in such efforts can be significantly

enriched by considering research accomplishments

on artificial intelligence, musical acoustics, room

acoustics, and music embodiment.

2.1.3 Artificial Intelligence in Music

Researchers in MTA have examined generative

deep learning methods since their early

developments, mainly regarding the possibilities

that such methods offer for human control of the

musical output. This control could either be in the

form of pre-designed parameters, e.g., for

generating drum rhythms by adapting to new

contexts that they have not encountered during

training by proper annotation of compositional

conditions, [27]. Such models exhibited some

interesting adaptations to time signatures that were

not included during training. Or through real-time

interaction, either by changing rhythm and pitch-

related parameters by allowing the user to move a 2-

dimensional square within some predefined limits,

or by allowing musicians to improvise on a given

chart and having the system to create the proper

piano accompaniment, [28].

Additionally, such methods have proven useful

for visualizing large datasets under different

perspectives that are set by the user. Methods based

on Long Short-Term Memory (LSTM) and more

recent Transformer-based approaches have

produced interesting results that allow users to

explore large databases of jazz standard song charts

(i.e., scores with chord symbols) according to a

wide range of user-defined criteria. As in the case of

music generation, sequence learning methods

capture some high-level features that allow human

interaction and “communication” of some basic

concepts. For instance, given proper annotations,

some high-level features can be inferred from the

context, e.g., transformers were able to capture the

concept of 12-bar blues when trained to identify the

blues form from “raw” symbolic representation of

memory (without explicit annotations about the

length of a piece in bars).

Recent machine learning methods have exhibited

impressive results that have been developed to demo

or product-level by worldwide recognized

companies and universities. Such examples include

the ChatGPT family of dialogue systems and

DALLE 2 for image generation, among many

others. Even though such deep learning methods

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capture high-level concepts, there is an open

question about the extent to which the result they

produce can be creative, [29]. To put the question

differently, can those methods create new

conceptual relations that can capture unlearned but

“entailing” interpretations that explain new, unseen

data in new ways, or allow the generation thereof

that is not only (impressively) adhering to specific

(learned) norms, but also actively recombining

knowledge in a way that reveals something new?

This is what creative humans do naturally, even

without the need for huge training datasets; imagine

what low quantities of “data” were available to J.S.

Bach.

A model that describes and explains human

creativity is Conceptual Blending, [30]. This model

has inspired the development of theoretical methods

that explain musical creative processes, [31] and

computational methods (based on Goguen’s

formulation, [32]) for music generation (among

other fields) that combine rule-based and

“traditional” machine learning approaches. Those

methods learn specific components of harmonic

knowledge from data and can recombine those

components effectively, creating new harmonic

spaces that integrate learned parts in new

arrangements that are justified through the

preservation of well-established musical principles,

described through rule-based formulations.

This generative combined approach has led to

the development of the CHAMELEON melodic

harmonization assistant, which has been evaluated

by composers of different levels, as an assistive tool

for making melodic harmonizations, [33]. Except

for that, a similar approach has been examined for

the cross-harmonization of jazz standards, [34],

where the melody of one song is harmonized with

the harmonic space of another song. Similar

approaches have been examined for melody and

drums rhythm, generation. In the latter two

approaches, the “creative part” involved blending

high-level features of melodies and drum rhythms.

The “generative” component, i.e. rendering those

features to music, was carried out by genetic

algorithms that targeted the high-level features

mentioned above in their fitness functions.

What sets such research apart from current trends

in machine learning generative methods is the fact

that the creative component is separated from the

generative component. This approach in some sense

identifies the necessary creative processes on a

“System 2” level, while it takes advantage of the

“System 1” inherent properties of machine learning

methods. More details for this discussion can be

found in a popular article, [35].

2.1.4 Electroacoustic Music Composition -

Musical Instruments

MTA is represented by academic members in the

Hellenic Association of Electroacoustic Music

Composers (HELMCA), [36] and members of the

International Confederation of Electroacoustic

Music (CIME/ICEM), [37]. Electroacoustic Music

(EAM) is the artistic field that is in a constant search

for innovation, both during research and in the

application, of electroacoustic technology for the

aesthetic creation of sound forms and the musical

organization of sound, [38], [39], [40], [41]. Its

close relationship with the evolution of musical

technology, as well as contemporary aesthetic

searches, makes it a constantly renewed field of

research and artistic creation, [42]. The field of

EAM is a convergence of the techniques and

technologies of sound composition, processing, and

projection as well as aesthetic criteria and analytical

methodologies for the study and creation of sound

forms, [43].

From a historical perspective the first field of

applications in music technology is the invention of

new (in their time) musical instruments. Present

studies of design and invention integrate different

materials and technologies for sound production,

manipulation, and the diffusion of their sound.

These research studies present musical instruments

that can be electronic, electroacoustic, hybrid,

digital, for one or more musicians, «intelligent»,

extended, networked, automated, robotic, and

interactive, [44], [45]. Their design demands

knowledge and skills from various fields of science,

music, and technology, [44], [45], [46]. From the

beginning of the 20th century until today many new

musical instruments have been invented, but a few

of them have been survived and still in use today,

like Theremin, Onde Martenot, Analogue Modular

Synthesizers, and others. Over the past two decades,

there has been an ever-increasing interest in the

research, design, and use of new electronic musical

instruments, and members of MTA work in this

field.

2.1.5 Acoustic Ecology and Soundscape

Ecology

An artistic and in parallel scientific subfield of

research in MTA is acoustic ecology and

soundscape ecology, [47], [48]. Acoustic ecology,

also known as soundscape studies, is a field that

explores the interaction between humans and their

surroundings through sound, [49]. Soundscape

ecology investigates the acoustic connections

between various living organisms, including

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humans, and their environment, whether they reside

in marine or terrestrial ecosystems.

The soundscape terminology was established by

R. Murray Schafer, as the sonic environment,

technically any portion of the sonic environment

regarded as a field of study. The term refers to

actual environments or abstract constructions such

as musical compositions and tape montages,

particularly when considered as an environment,

[50]. Schafer’s terminology helps to express the idea

that the sound of a particular locality (its keynotes,

sound signals, and sound marks) can - like local

architecture, customs, and dress - express a

community’s identity to the extent that settlements

can be recognized and characterized by their

soundscapes, [49].

Acoustic ecology can set the base for a dynamic

monitoring instrument for the evaluation of the

environment by observing the acoustic signature of

species and landscapes, [51]. Acoustic indices are

increasingly being used when analyzing

soundscapes to gain information on biodiversity and

describe the environment. There has been

considerable interest and research to develop and

compute acoustic indices that represent the

characteristics of the soundscape, [52], [53], [54].

Ongoing research, in the frame of a PhD thesis,

focuses on the soundscape of the White Mountain in

Crete using analytical methods of acoustic ecology,

soundscape ecology, and ecoacoustics. The way

soundscape analysis can provide a feasible approach

for the environmental monitoring of the acoustically

unknown areas in the area, is investigated.

Additionally, the observation of the soundscape as a

monitoring system of an ecosystem function and

sustainability and the analysis of the acoustic

activity are introduced as methods which can

efficiently recognize the changes in the ecological

integrity and can be a valuable tool for

environmental management.

Furthermore, several research studies on specific

soundscapes of Crete and Greece have been

conducted in MTA in the framework of Bachelor

Theses. A mixed methodology of recording and

analysis has been developed and applied to those

projects. The study, recording, and preservation of

the sound diversity (analogous to biodiversity) of

protected natural areas aim not only to the

advantage of scientific knowledge but also to the

awareness of an ecological balance of the world

through sound. This aim is served through

soundscape musical composition that brings this

ideal balance to the focus of attention, [55].

2.2 Acoustics

The main research subfields of acoustics where the

MTA is significantly contributing and exhibiting

leading expertise are physical acoustics,

optoacoustics, ultrasonics, vibroacoustics, and

musical acoustics. These basic subfields are further

presented and analyzed along with the methods

applied.

2.2.1 Physical Acoustics, Optoacoustics,

Ultrasonics

Researchers of MTA in collaboration with the

Institute for Plasma Physics and Lasers (IPPL) of

the Centre of Research and Innovation of HMU are

experts in laser-matter interaction and have

developed whole-field dynamic laser interferometry

methods, [56], capable of studying the dynamic

behavior of the irradiated matter and the generation

and propagation of ultrasonic waves. These

interferometric methods have very high spatial and

temporal resolution, while nanosecond and

femtosecond laser pulses are used. A white-light

interferometry method has also been implemented to

monitor permanent damages and to evaluate the

ablation depth of the laser-irradiated samples.

Moreover, pump-probe transient reflectivity optical

setups, [57], have been developed capable of

studying the generation of nano-acoustic strains in

thin metallic films (such as Au, Ag, Ti, Ta)

deposited on substrates (such as Si, ZnO, glass)

using optoacoustic transduction on the laser

irradiated samples.

Researchers of MTA are experts in numerical

modeling and simulations of coupled multiphysics

problems using basic numerical methods like the

Finite Element Method (FEM), the Finite Difference

Method (FDM), and the Boundary Element Method

(BEM). Numerical methods can simulate

complicated processes, i.e., when the material

properties are anisotropic, viscoelastic, or

temperature-dependent, as well as complicated

geometries of dynamic structures. The numerical

methods have many advantages compared to the

analytical methods since the solution domain is

divided into many smaller domains that are allowed

to have different values of physical properties

and/or varying loading conditions. FEM is ideal for

solving complicated multiparametric problems like

laser-matter optoacoustic interactions. The

researchers of the Department have carried out

multiphysics FEM simulations to investigate the

interactions of pulsed lasers (ns, ps, or fs duration)

with thin solid films and study their dynamic

thermomechanical behavior, [56] and their

transduction efficiency. The FEM model is capable

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to compute the phase changes of matter, to

concerning the material properties, as well as to

provide detailed insights into physical quantities

such as displacements, temperatures, velocities,

stresses, and plastic strains at every spatiotemporal

solution time step. These simulations can monitor

the generation and propagation of surface acoustic

waves (SAWs), [56] and longitudinal waves, [57]

and are validated by experimental interferometric

measurements and pump-probe techniques

developed by researchers of the Department.

To study the dynamic behavior of the irradiated

samples via FEM numerical simulations a CAD

geometry of the structure to be irradiated is first

created, then a fine mesh geometry is generated, and

transient Multiphysics coupled thermal-structural

analysis takes place. The heat conductivity and

mechanical wave propagation equations are solved,

while the laser source is considered as a heat source

loading term for the simulations. Elastoplastic

material properties as well as temperature-

dependent material (thermal and mechanical)

properties are considered for the simulations. The

modeled structure can be any type of material such

as metal, polymer, semiconductor, composite, or

metamaterial. The appropriate boundary conditions

should be also provided for the developed model.

An important aspect of the developed models is that

the Lagrangian mesh may be locally adaptive

depending on the simulation needs.

The recent development of compact and cost-

effective nanosecond, picosecond, and femtosecond

laser systems, capable of inducing breakdown in

ambient air or other gases and solids, has facilitated

the widespread integration of laser-plasma sound

sources across scientific and industrial domains.

Typical LPSS applications include, among others,

Laser-Induced Breakdown Spectroscopy (LIBS),

[58], non-destructive testing and diagnostics [59],

signal transmission for underwater or air-water

communication, and military applications, [60]. The

Department has made significant contributions and

exhibits leading expertise in the study,

characterization, and exploitation of laser-plasma

sound sources in ambient air. Laser-plasma sound

sources (LPSSs) in ambient air are generated by

optoacoustic transduction of fast (nanosecond) or

ultrafast (picosecond - femtosecond) laser pulses

with sufficient energy focused on a gas gaseous,

liquid, or solid target. In ambient air, laser-induced

breakdown (LIB) with consequent generation of an

LPSS requires optical intensities above the threshold

of approximately 2×1011 W/cm2. Laser breakdown

triggers a fast thermalization process in the air

which results from the interaction of hot free

electrons with heavy particles (ions, atoms, and

molecules) within the ionization volume. The

thermalized air bubble undergoes rapid expansion

and elastic contraction, hence generating rapid

pressure fluctuations that lead to the emission of an

acoustic pulse. After the emission and propagation

of the pulse away from the source, the medium

returns to its initial state. The acoustic pulse has a

characteristic time-domain profile of an N-pulse

with a duration that can span from a few

microseconds to tens of microseconds, depending

on the characteristics of the incident laser radiation.

Concurrently, the excited volume emits light due to

luminescent processes and localized thermal

excitation. Previous research carried out by MTA in

collaboration with the IPPL of the HMU and the

Electrical and Computer Engineering (ECE)

Department of the University of Patras (UP) has

revealed a correlation between the optical and

acoustic signals generated by laser-induced

breakdown in air, enabling the prediction of the

acoustic emission of the LPSSs from the respective

light emission.

In terms of frequency content, the generated

acoustic N-pulse exhibits a low-end response with a

first-order high-pass profile (see also Results

section). For nanosecond laser pulses, this spectral

range extends from subsonic frequencies (< 20 Hz)

to the upper audible frequency range (~ 20 kHz) or

the near ultrasounds (< 50 kHz). Femtosecond laser

pulses typically yield a wider frequency range that

extends well into the mid-ultrasound range (> 500

kHz), at a cost of reduced acoustic energy within the

audible range. At the high-frequency end of the

spectrum, the acoustic N-pulses exhibit a well-

defined response that diminishes with increasing

frequency. The peak pressure levels achieved by

LPSS can exhibit significant variation, ranging from

barely perceivable (a few decibels) to exceptionally

loud (130 dB or higher). This variability

predominantly depends on the total optical energy

deposited into the targeted medium, such as ambient

air. MTA and the aforementioned partners have

carried out extensive research on the correlation

between laser radiation characteristics, namely laser

pulse energy, duration, wavelength, and focusing

conditions, and acoustic pulse characteristics,

particularly acoustic pulse pressure, energy, and

duration, [61].

Additionally, the geometry of laser-plasma sound

sources ranges from a completely spherical (point-

like) configuration to an elongated one (cylindrical

or line-like). This variability is contingent upon the

characteristics of the incident laser radiation and

results in acoustic emissions spanning the entire

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range from fully omnidirectional to highly

directional. Specifically, tightly focused short laser

pulses, typically in the nanosecond range, create

point-like plasma sources characterized by

dimensions of the order of a millimeter emitting

spherical sound waves. Conversely, the loose

focusing of ultra-short laser pulses, in the

picosecond or femtosecond range, gives rise to the

formation of line-like sources, commonly known as

laser-plasma filaments. These filaments typically

possess sub-millimeter thickness and lengths that

can vary from a few millimeters to hundreds of

meters, contingent on the laser pulse energy and

focusing conditions. Laser-plasma filaments

produce cylindrical acoustic waves known for their

distinct directional and propagation characteristics.

Recently, MTA and IPPL and collaborating partners

have introduced a computational model that allows

for the estimation of the acoustic directivity of the

resulting plasma source in the far field, considering

its specific geometry, [61]. Also, the team has

shown that energy deposition by non-linear

interaction of femtosecond laser pulses with ambient

air can be effectively modeled by use of Particle-In-

Cell (PIC) codes, [59]. Moreover, a research group

including members of the MTA has proposed the

exploitation of optoacoustic transduction for the

reproduction of audio signals via massless, spatially

unbound, movable, and potentially remote LPSSs,

[62], [63].

2.2.2 Vibroacoustics, Musical Acoustics

MTA is among the pioneers in the study of musical

instruments using laser-based interferometric

techniques. Experimental modal testing allows the

identification of modal parameters of vibrating

musical instruments such as natural frequencies,

mode shapes, and modal damping for the

substructures of the musical instruments. Modal

analysis is a combination of a method for the

calculation of the frequency response function

(FRF) of the vibrating instrument and the

visualization of the resonant modes. This analysis

has been the topic of numerous studies in the past

years, [64], [65].

A widely used modal analysis technique is based

on the impulse response measurements utilizing an

impact hammer and an accelerometer, [66]. Using

the signals of the applied force and a kinematic

quantity (e.g. acceleration) the FRF can be

calculated by various estimators, which are ratios of

the auto and cross spectra of the input and output

signals. These measurements can provide the FRF,

which contains information about the frequencies

where the resonances occur and can lead to

vibration characteristics of the system such as

damping etc. On the other hand, they do not provide

any information concerning the vibration mode

itself, e.g., how the system vibrates and the

amplitude distribution. For this, holographic

methods are applied, which are also used in the field

of musical acoustics, [66], [67], [68], [69].

An interferometric technique, namely Electronic

Speckle Pattern Interferometry (ESPI), [66], is used

in the Department for the visualization of vibration

modes. It is a technique that uses laser light together

with detection video recording and processing to

visualize static and dynamic displacements of the

measuring objects. A brief description of ESPI

follows. A laser beam is split in two. The first,

illuminates the vibrating object, the reflected beam

is combined with the second beam, and the resulting

hologram is captured by a CCD camera. The

resulting images contain spatial information of the

vibrating surface, along with amplitude information

of the perpendicular to the surface vibration. The

combination of the impulse response measurement

with the ESPI results provides a detailed description

of the vibrating instrument.

Finite element analysis is ideal for predicting

how musical instruments respond to any kind of

force loads, vibrations, and variations in

environmental conditions (temperature, relative

humidity, etc.), [70], while in musical acoustics, the

BEM formulation is commonly used to calculate the

sound radiated by simulated musical instruments,

[71]. Researchers in the Department have simulated

the vibroacoustic behavior of string musical

instruments such as the violin and the bouzouki,

[66], as well as the vibroacoustic behavior of

percussion musical instruments, [17], [72]. The

vibroacoustic behavior of the string musical

instruments has also been validated by experimental

measurements using ESPI, impact hammer, and

psychoacoustic tests, [66], thus a collaboration of

researchers performing simulations and experiments

has been accomplished in the Department. In the

study of the vibroacoustic behavior of cymbals via

FEM-BEM simulations, motion experiments have

also been performed providing accurate data for the

drumstick cymbal interaction, [17]. Results of this

study may be further used as reverse engineering

inputs, to machine learning models for the

estimation of geometrical and mechanical

parameters of cymbals from audio signals,

enhancing the collaboration between the faculty

members of the Department.

Regarding the methodology for studying the

vibrational and acoustic behavior of musical

instruments via numerical simulations a CAD

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geometry of the musical instrument is initially

created, based on the real geometric characteristics

or via 3D scanning, then a fine mesh geometry is

generated and various numerical analyses: such as

modal, frequency response function, harmonic, [73],

in the frequency domain as well as time domain

FEM-BEM vibroacoustic analysis, [17], can later be

performed depending on the needs of the research.

For all these different simulations proper material

properties, loading, and boundary conditions are

provided to the developed model.

3 Results

The research in the scientific areas and the subfields

described above, has led to novel results and

cutting-edge applications. Representative research

results and key findings are presented here.

3.1 Music and Sound Technology

3.1.1 Music Embodiment

Regarding the aforementioned study (see section

2.1.1) which explores the embodied aspects of

Hindustani/Dhrupad vocal music, the findings

indicate that while Dhrupad singers exhibit some

variability in how they use their hands during

singing, there remains a level of consistency in how

they associate effort with different melodies and

types of gestural interactions with imaginary

objects. However, various cross-modal associations

are identified among the vocalists, which depend on

the specific melodic mode (raga), the mechanics of

vocal production, the structure of the improvisation

(alap), and analogies between different domains.

For instance, in the case of Dhrupad vocalist

Afzal Hussain, the effort level exerted by the

vocalist’s body is associated with:

1. The pitch range within each octave, specifically

the highest note reached during the ascending

portion of the melodic glide (whether in the upper or

lower part of the octave).

2. The melodic intention to ascend or move

toward the tonic note in the subsequent melodic

progression.

3. The size or interval of the ascending portion of

the melodic movement.

4. The asymmetry between ascending and

descending melodic glides, where ascending

corresponds to an intensification in effort, and

descending corresponds to an abatement in effort.

There is an observed trend for interactions with

elastic objects (stretching or pushing-compressing)

to be more effortful than interactions with rigid

objects (pulling gestures: medium levels; throwing:

low; two types of grips: effortless).

Additionally, in terms of gesture classification,

results have highlighted specific cross-modal

association trends between gesture classes

(interactions with elastic versus rigid imaginary

objects), effort levels (ranging between 0-10, 10

being the highest), melodic intention (ascent vs.

descent) and octave pitch range (low, high part of

octave); the latter being closely related to more

stable vs. unstable melodic movements for each

melodic mode (raga Jaunpuri).

The findings obtained from all participants also

demonstrate the feasibility of creating concise

models for estimating effort and classifying gestures

using a limited set of statistically significant

acoustic and movement features derived from the

original data. Two variations of these models were

developed: (a) one tailored to best suit each

performer and (b) one designed to capture shared

behaviors across different performers. Variations

between these models can be attributed to either

unique gestural styles, suggesting the need for an

individualized approach for each performer, or a

weakness of the selected low-level features in

capturing the fundamental aspects of the responses.

The latter could be due to the restricted dataset size

and the challenge of quantifying subjective aspects

that involve some level of interpretation.

In essence, these findings underscore a

substantial connection between effort and melodic

characteristics, although the relationship with

performers’ hand gestures appears to be less

straightforward, more personalized, and potentially

less consistent. For example, the subsequent

analytical representation of effort levels relies on a

general linear model derived from the original data,

which exhibits a higher degree of overlap across

performers in terms of the number and type of

features. It holds that, [14]:

𝑒𝑓𝑓𝑜𝑟𝑡 = −2.13 ∗ 𝑓1 + 2.3 ∗ 𝑓2 – 1.34 ∗ 𝑓3 + 0.93 ∗

𝑓4 + 0.65 ∗ 𝑓5

where:

f1 = starting minimum pitch on a relative

logarithmic scale ( about the tonic).

F2 = maximum pitch on an absolute logarithmic

scale (pitch height).

F3 = mean value of velocity calculated on the mean

position of the two hands.

F4 = SD of velocity calculated on the mean position

of the two hands.

F5 = mean hand distance according to handedness,

only used for one of the singers.

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Figure 3 provides a schematic representation of

parameter mapping, highlighting the potential

integration of the linear model into evolving

Electronic Musical Instruments. It visually

represents the changes in acoustic and movement

features based on different levels of user effort.

These findings could inform the optimization of

Electronic Musical Instruments by incorporating

effort as an intermediary mapping layer. This

naturally prompts discussions about the possibility

of utilizing physiological data as a more intuitive

measure of effort in wearable technologies.

Fig. 3: Schematic overview of potential parameter

mapping in new Electronic Musical Instruments,

[14]

3.1.2 Networked Music Performance

MTA has contributed to the research domain of

NMP by presenting one of the early systems for

real-time music collaboration, called DIAMOUSES.

As realized through dedicated experiments,

DIAMOUSES can support a wide range of music

performance scenarios including music rehearsals,

music lessons, and jamming sessions, [23].

DIAMOUSES is presented as one of the first

systems investigating remote musical interactions at

the ‘Networked Music Performance’ article on the

Wikipedia and has produced some of the first

graphical user interfaces for NMP, [23], such as the

one shown in Figure 4.

In the years that followed, MTA contributed to

the MusiNet project, which used the Opus low-

latency audio codec to ease communication

throughout commonly available network

infrastructures. Part of this research was devoted to

investigating the perspective of NMP collaborations

in traditional music, thus promoting NMP as an

enabling technology for remote ethnic groups to

widely disseminate their musical culture. An

attempt of a Cretan music performance conducted

through the network is depicted in Figure 5.

In the post Covid-19 era, NMP research

endeavors in MTA were devoted to the development

of the MusiCoLab platform for online music

education. MusiCoLab provides a suite of

collaborative, web-based applications supporting

online music teaching and learning by facilitating

musical artifacts such as music scores (shown in

Figure 6) and backing tracks (shown in Figure 7) as

collaborative objects that may be manipulated and

transformed during synchronous and asynchronous

music learning. A distinct focus of MusiCoLab is to

make use of current achievements in artificial

intelligence for automatic music content analysis to

enhance engagement in music learning, [74].

Fig. 4: Graphical User Interface of the

DIAMOUSES project during a networked music

rehearsal

Fig. 5: Traditional music performance conducted

through the network using the MusiNet

infrastructure for NMP. Preparation of musicians to

before the experiment (Left). One of the musicians

collaborating through the network (Right)

Fig. 6: Collaborative transformations of music

notation artifacts in MusiCoLab

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Fig. 7: Collaborative music learning using a backing

track in MusiCoLab

3.1.3 Artificial Intelligence in Music

The most interesting results regarding AI in music

have been produced by the development of the core

method that performs conceptual blending in

musical cadences, [75].

This method is the basis for building compound

harmonic spaces that allow melodic harmonization.

Building those compound spaces is closely related

to what human composers do: we get a new concept

(e.g. the tritone substitution) by recombining things

that are already known (perfect and phrygian

cadence) and use it in a way that reveals something

new about our world (i.e. that both-direction

resolutions are possible). Figure 8 shows the voice

relations that are involved in generating this result,

with bold lines/connections indicating strong

relations and thin lines weak but considerable

relations. This new concept can be employed in

various contexts provided by the melody. With this

method, the generative and the creative processes

are distinct: the generative part is creating the

concrete chords for a given melody; the “creative”

part has initially figured out the new component that

provides alternative interpretations of what we

already know (about how chord transitions work

within the initial spaces).

Fig. 8: Conceptual Blending of the Perfect and the

Phrygian cadences creates the tritone substitution

and the backdoor progression chord sequences

Fig. 9: Melody with segments in the C and F#

tonalities harmonized with the blended C and F#

Bach chorale major spaces

This method has produced results in various

directions, [76], regarding the creative exploration

of harmonic spaces, including blending of harmonic

idioms that are “incompatible” (meaning that they

have few common or similar chords) and blending

of harmonic spaces that result from transposed

versions of the same harmonic space (e.g. the

harmonic space of the Bach Chorales in C and F#

major tonalities). Especially in the latter case, the

method proved useful for producing harmonic

solutions for harmonizing melodies with implied

harmonic characteristics that are inconsistent with a

given harmonic space. The example in Figure 9

shows such an example, where segments of the

melody are composed in different tonalities.

Blending the harmonic space of the Bach Chorales

in C and F# major tonalities provides a solution to

this unconventional melody (regarding the style of

Bach Chorales) that recombines what is already

known (i.e. the space of Bach Chorales) and reveals

something new (i.e. that such remote tonalities can

be bridged by manipulating specific components of

the existing space).

Fig. 10: Two example cross-harmonizations with

“Solar” as Song A and (a) Giant Steps and (b) Time

Remembered as Song B. Melody mismatches are

indicated. In (a), melody mismatches are very often,

in contrast to (b)

The application of the method that was adjusted

for generating cross-harmonizations of jazz

standards (i.e. melody of one song reharmonized

based on the harmony of another), indicated that

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there are still details in the generative part (i.e. the

part that applies chords to the given melody) that

need to be corrected. Figure 10 (taken from [34])

shows that in some cross-harmonizations, some

produced chords harmonize melodic segments that

include notes that are “incompatible” with the

chord; identifying those incompatibilities, however,

would require the formulation of extensive sets of

rules for each chord. It is a future challenge to

examine how deep learning models would help

toward implicit learning of such rules from data.

3.2 Acoustics

3.2.1 Physical Acoustics, Optoacoustics,

Ultrasonics

Representative results of pump-probe experimental

measurements along with FE results are shown in

Figure 11 from a research collaborative work of the

MTA researchers with IPPL, that studies the

photoacoustic transduction on Ta and Ti thin films,

[57]. Figure 11a presents comparative transient

reflectivity signals from Ta and Ti thin films coated

on Si <100> substrates. The reason behind the larger

modulation depth on the Ta sample, compared to the

Ti sample, is the result of enhanced photoacoustic

transduction. Figure 11b shows the time evolution

of the strain penetrating the substrate at 4 nm depth

from the interface with the metal, as calculated by

the FE model for both Ta/Si and Ti/Si samples. Due

to the larger Young’s modulus of Ta higher strains

are developed. Both experimental and simulation

results demonstrate that Ta exhibits superior

photoacoustic properties.

Fig. 11: a) Measured transient reflectivity signals for

the 25 nm Ta and Ti films on Si substrates showing

the electron excitation and Brillouin oscillations

characteristic feature, b) Calculated strain

distribution in 25 nm metal/Si systems as a function

of time, for a depth of 4 nm in the Si substrate.

Regarding the research on simulated

optoacoustic interactions, Figure 12a shows

representative results of the vertical component of

displacement for three different distances from the

center of an irradiated Si target via a 6 ns laser

pulse. Moreover, Figure 12b shows characteristic

contour plot results of vertical displacement for

three different temporal moments. The laser

intensity is in the thermoelastic regime, and it is

evident that an SAW is generated and propagates

through the sample. The speed of ultrasonic waves

can be calculated by dividing the distance between

two nodes by the time required for the wave to

propagate from one node to the other. Thus, the

propagation speed is calculated to be ~5000 m/s.

This value has low deviations from the value of

5200 m/s, which is found when using the analytical

equation found in [77], for SAW. A thermal

structural FEM simulation was performed, to obtain

this result, considering temperature-dependent

thermal and mechanical properties, while the laser

source of Gaussian distribution is considered as the

heat source loading term.

Regarding research on LPSS, new results from

the study of the acoustic directivity of LPSSs

generated by strongly focused nanosecond pulses

are here presented. Figure 13a shows a schematic

diagram of the LPSS measurement process. The

LPSSs are formed by laser pulses with a 6 ns

duration of 532 nm wavelength and 120 mJ energy,

focused by a 75 mm lens. For the acoustic

measurements, a high-dynamic range and broadband

microphone are used (G.R.A.S 46BE) connected to

a high-sampling rate audio interface (RME Fireface

802). Recording is done by Audacity software.

Figure 13b shows the acoustic directivity of the

generated laser-plasma sound sources measured at

12 cm from the source. The polar diagram of Figure

13b is calculated by taking the total acoustic energy

of the measured N-pulses on the azimuthal angles 0,

30, 60, and 90 degrees. The emission towards the

directions from 120 to 180 degrees is extrapolated

considering cylindrical symmetry. The source

exhibits a mild directionality as the acoustic

emission on the laser propagation axis is

approximately 5 dB lower than on the axis

perpendicular to the laser path. This is expected

since, as it is well known, even with strong focusing

the plasma source exhibits a slightly elongated

geometry that mainly occurs due to plasma back-

propagation, [78].

However, the directional characteristics of the

source become weaker with increasing observation

distance. Figure 14a shows the acoustic directivity

of the LPSS generated under the same conditions,

this time measured at 60 cm. Figure 14b shows the

respective acoustic frequency spectra where, for

reasons of completeness, three measurements on the

xz plane at elevation angles 30, 60, and 90 degrees

are also plotted. From the two diagrams, it becomes

evident that the plasma source exhibits an almost

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perfect omnidirectional emission at 60 cm distance,

with a maximum deviation of less than 1 dB

between different angles. The measurements on the

xz plane validate the assumption of cylindrical

symmetry used to extrapolate the data.

Fig. 12: a) The evolution of vertical displacement

for three different distances from the center of an

irradiated Si target in the thermoelastic regime. b)

Contour plots of vertical displacement for three

temporal moments

Fig. 13: a) Schematic diagram of the process for

measuring the acoustic directivity of LPSSs, b)

acoustic directivity of LPSSs generated by strongly

focused nanosecond laser pulses with 6 ns duration,

532 nm wavelength and 120 mJ energy measured on

the xy plane at 12 cm distance

Fig. 14: a) Acoustic directivity of LPSSs generated

by strongly focused nanosecond laser pulses with 6

ns duration, 532 nm wavelength, and 120 mJ energy

measured on the xy plane at 60 cm distance and b)

respective acoustic frequency spectra with

additional xz plane measurements

3.2.2 Vibroacoustics, Musical Acoustics

Figure 15 shows the combination of impulse

response measurements along with ESPI results on a

carbon fiber bouzouki. The red dot highlights the

matching between a resonance frequency in the FRF

plot provided by impulse response measurements,

and the related vibration mode, as given by ESPI,

[65].

Figure 16 shows representative numerical results

of a drumstick-cymbal interaction via time-domain

FEM-BEM simulations, [17]. Figure 16a shows the

pressure distribution of the cymbal 3 ms after being

hit by the drumstick, while Figure 16b demonstrates

the computed normalized pressure at a point located

in the air. For this vibroacoustic simulation, the

drumstick body is considered a rigid body, while the

cymbal is considered a deformable body. A marker-

based motion capture (mocap) system is used for

capturing the real loading conditions to be used in

the FEM-BEM simulations during the drum-stick-

cymbal interaction. The recorded velocity and the

spatial coordinates of the drumstick and the cymbal

are adopted by the FEM-BEM model. FE is used to

model the 8-inch splash B20 bronze alloy cymbal,

while BE is used to model the surrounding air. The

point where the normalized sound pressure, arising

from the impact, is computed corresponds to an

assumed microphone position in the acoustic field.

Fig. 15: Combination of a frequency response

function and an ESPI image for the visualization of

the vibration mode, which is excited at the

resonance highlighted with the red dot

Representative results of ESPI experimental

measurements along with FE results are shown in

Figure 17, as part of a collaborative research effort

by MTA researchers investigating the vibroacoustic

characteristics of cymbals, [72]. Different CAD

approaches are used to model the complex geometry

of a curved splash cymbal. The CAD model

indicates the critical points of curvature changes and

allows for a good approximation by two three-point

arcs, able to interpolate the cymbals’ curved

geometry. A uniform thickness is assumed for the

developed geometry. Figure 17 shows the

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experimental and numerical modal results that

present better agreement. The FEM cymbal model

has a thickness of 1.3 mm.

The experimental techniques that have been

previously described in combination with numerical

calculations can fully describe the vibrational and

acoustical behavior of musical instruments. The

application of the methods’ combination during the

manufacturing process of musical instruments may

lead to assemblies of optimized vibration

characteristics.

Fig. 16: a) Pressure distribution of the splash

cymbal 3 ms after the initiation of the drum-stick-

cymbal interaction, b) Time domain FEM-BEM

results of sound pressure for the splash cymbal

Fig. 17: Four vibration modes and the related

resonant frequencies using ESPI and FEM

Regarding local Cretan Music, researchers of the

department conducted a vibrational analysis of the

Cretan lyra top plate, using ESPI, impulse response

analysis, and FE analysis. The vibration amplitude

distributions obtained by time average ESPI for

each eigenfrequency were in good agreement with

the predicted ones from the FE analysis. A

vibrational study for the full Cretan lyra assembly

was also performed, [63]. Time-average ESPI

experimental vibration analysis of the main normal

modes of two Cretan lyras of different periods, was

performed. The first one was a pear-shaped Cretan

lyra of 17th century and the second was a

contemporary one. The results showed that the

variations in observed normal frequencies were

primarily attributed to distinct geometric

characteristics and shapes. In general, the 17th-

century pear-shaped lyra was smaller in all

characteristic dimensions compared to its

contemporary counterpart. Consequently, its

resonance frequencies were higher for the

corresponding characteristic normal mode.

4 Conclusion, Discussion, and

Research Prospects

Research in MTA maintains a highly

multidisciplinary character that produces results in a

wide range of fields spanning from the area of

optoacoustics to human embodiment and artificial

intelligence. The unique and broad specializations

of the research and personnel, in combination with

high-end equipment that is available for studying

the physical properties of sound and music, have

opened new perspectives towards novel research in

music technology and acoustics. The scientific

novelty and cutting-edge know-how accumulated by

the members and groups provide strong potential for

the transition towards impactful scientific

applications. Such a transition is expected to be

further amplified by broader collaborations and the

incorporation of mature application-level scientific

components, such as artificial intelligence, in a

wider range of research activities within MTA.

Particularly, the research in MTA on percussion

sound classification by use of artificial intelligence

is quickly evolving towards product-level maturity.

The very limited relevant literature, [79], [80] shows

that the field is not explored and is highly

prospective in this direction, with future work

focusing on the formation of a large sound database

via a programmable motorized drummer machine.

Frequency response function modal analysis of

percussion instruments, such as cymbals, and

idiophones, and corresponding measurements of the

instrument’s sound spectrum will be performed. The

large sound database will be used to train machine

learning models to identify different geometrical

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characteristics and material properties of the

instruments from their acoustic response. The

efficient drummer machine will enable the fast

formation of large databases of objective

measurements, effectively eliminating the subjective

human factor. This sound database will constitute a

reference for the validation of vibroacoustic

numerical models estimating percussion

instruments' sound synthesis.

Furthermore, the MTA team working on LPSS is

currently developing a novel method for the precise

characterization of phononic crystals and acoustic

metamaterials, [81], that is anticipated to

significantly boost the design and adoption of

acoustic meta-structures in real-world applications.

LPSS exploitation in acoustic perception

measurements, biomedical engineering e.g.,

ultrasound focusing for noninvasive treatment, and

study of sound-mater interaction by laser-driven

excitation of acoustic meta-structures is planned for

the near future. This optimal acoustic excitation tool

will be used complementarily to the motorized

excitation system for the evaluation of idiophones,

e.g., percussive instruments, with unprecedented

advantages, especially in real-time acoustic

monitoring of the manufacturing process.

Also, future work includes fusing pre-trained

models of images of vibrational modal patterns of

given 3D models, [82] and materials with music

instrument simulation tools, [83] into a complete

tool for the estimation of the impact of different

materials and structures in the sound of musical

instruments. Accurate implementation of such

models would allow instrument manufacturers to

make justified decisions during the instrument

design phase, but also to explore new materials,

shapes, and structures. Such a computational tool,

together with the ESPI method developed in MTA

for the experimental determination of vibrational

modes of acoustic musical instruments, will

constitute a complete solution for optimization and

innovation in musical instrument design.

Moreover, many possibilities emerge from the

idea of leveraging pre-trained machine learning

models in specific modalities and fusing them

through fine-tuning processes that require few

examples for training. One such example is the

development of a model that relates upper-body

gestures of Hindustani vocalists collected from

video and motion capture systems with raga-specific

melodic information of the singing voice. Such

models could prove useful for the accurate large-

scale automatic annotation of special gestures in

audio-visual datasets with Hindustani music,

allowing, for instance, users to search for

audiovisual segments within large databases

incorporating specific gestures (possibly performed

by the user as a system-input query). These research

approaches can be implemented both in situ and

remotely, taking advantage of the networked music

performance, as presented. Additionally, room and

soundscape acoustics may be also considered to

enable studies of human-environment music and

sound acoustics interactions.

The unique multidisciplinary approach to the

physics of sound, audio technology, and artistic

aspects of music exercised in MTA, together with

the growing collaboration between the various

groups of the Department, is expected not only to

broaden scientific interest in music technology and

acoustics but also lead to unique product-level

results.

Acknowledgement:

This work was supported by computational time

granted by the Greek Research & Technology

Network (GRNET) in the National HPC facility

ARIS under project ID pr013024-LaMPIOS II. S.

Brezas acknowledges the Hellenic Mediterranean

University, for the funding within the project

«Recording and metrological analysis of the

vibroacoustic characteristics of musical instruments

for the investigation of alternative and low-cost

materials and geometries with relevant sound

characteristics». The publication fees are financed

by the Project “Strengthening and optimizing the

operation of MODY services and academic and

research units of the Hellenic Mediterranean

University”, funded by the Public Investment

Program of the Greek Ministry of Education and

Religious Affairs.

References:

[1] Hellenic Mediterranean University, [Online].

https://mta.hmu.gr/en/home/ (Accessed Date:

November 1, 2023).

[2] Acoustics and Music Technology - BSc

(Hons), [Online].

https://www.eca.ed.ac.uk/study/undergraduat

e/acoustics-and-music-technology-bsc-hons

(Accessed Date: November 1, 2023).

[3] Acoustics with Music, [Online].

https://www.southampton.ac.uk/courses/acou

stics-with-music-degree-bsc (Accessed Date:

November 1, 2023).

[4] Hosken D., An Introduction to Music

Technology, Routledge, 2014, DOI:

10.4324/9780203539149.

WSEAS TRANSACTIONS on ACOUSTICS and MUSIC

DOI: 10.37394/232019.2024.11.1

Spyros Brezas, Stella Paschalidou,

Chrisoula Alexandraki et al.

P-ISSN: 1109-9577

Volume 11, 2024

[5] Mazzola G., Pang Y., Heinze W., Gkoudina

K., Pujakusuma G.A., Grunklee J., Chen Z.,

Hu T., Ma Y., Basic Music Technology: An

Introduction, Springer Cham, 2018,

DOI:10.1007/978-3-030-00982-3.

[6] Beranek L.L., Mellow T., Acoustics: Sound

Fields, Transducers and Vibration,

Academic Press, 2019.

[7] Alton Everest F., Pohlmann K.C., Master

Handbook of Acoustics, McGrawHill

Education Tab, 2021.

[8] What is Acoustics?, [Online].

https://acoustics.byu.edu/what-is (Accessed

Date: November 1, 2023).

[9] Leman M., Embodied music cognition and

mediation technology, MIT Press:

Cambridge, 2008, DOI:

10.7551/mitpress/7476.001.0001.

[10] Noë A., Action in perception (Representation

and mind), MIT Press: Cambridge, 2004.

[11] Varela F., Thompson E., Rosch E., The

embodied mind: Cognitive science and

human experience, MIT Press: Cambridge,

1991.

[12] Goldstein E., Sensation and Perception,

Wadsworth Publishing, 2016.

[13] Paschalidou S., Miliaresi, I., Multimodal

Deep Learning Architecture for Hindustani

Raga Classification, Sensors and

Transducers, Vol. 261(2), 2023, pp. 77-86.

[14] Paschalidou S., Effort inference and

prediction by acoustic and movement

descriptors in interactions with imaginary

objects during Dhrupad vocal improvisation,

Wearable Technologies, Vol. 3, 2022, e14,

DOI:10.1017/wtc.2022.8.

[15] Antoniadis P., Paschalidou S., Duval A.,

Jgo J.F., Bevilacqua F., Rendering

embodied experience into multimodal data:

concepts, tools and applications for Xenakis’

piano performance, Proceedings of the

International Centenary International

Symposium XENAKIS 22, Athens & Nafplio,

2022.

[16] Holzapfel A., Hagleitner M., Paschalidou S.,

Diversity of traditional dance expression in

Crete: Data collection, research questions,

and method development, Proceedings of the

First Symposium of the ICTM Study Group

on Sound, Movement, and the Sciences

(SoMoS), Stockholm, 2020.

[17] Kaselouris E., Paschalidou S., Alexandraki

C., Dimitriou V., FEM-BEM Vibroacoustic

Simulations of Motion Driven Cymbal-

Drumstick Interactions, Acoustics, Vol. 5(1),

2023, pp. 165–176, DOI:

10.3390/acoustics5010010.

[18] Leman M., Music, Gesture, and the

Formation of Embodied Meaning. In Musical

gestures: Sound, movement, and meaning,

Routledge, 2010.

[19] Mulder A., Design of Virtual Three-

dimensional Instruments for Sound Control,

PhD thesis, Rijks Universiteit Groningen,

1998.

[20] Castagne N., Cadoz C., A goals-based

review of Physical modelling, Proceedings

of the 2005 International Computer Music

Conference (ICMC 2005), Barcelona, 2005,

pp. 343-346.

[21] A NIME Reader, Fifteen years of new

interfaces for musical expression, Springer

Cham, 2017, DOI: 10.1007/978-3-319-

47214-0.

[22] Habiter (avec) Xenakis, [Online].

https://www.cda95.fr/fr/agenda/habiter-avec-

xenakis (Accessed Date: November 1, 2023).

[23] Alexandraki C., Akoumianakis D, Exploring

New Perspectives in Network Music

Performance: The DIAMOUSES

Framework, Computer Music Journal, Vol.

34(2), 2010, pp. 66-83, DOI:

10.1162/comj.2010.34.2.66.

[24] Wu X., Dhara K.K., Krishnaswamy V.,

Enhancing Application-Layer Multicast for

P2P Conferencing, Proceedings of the 4th

IEEE Consumer Communications and

Networking Conference, 2007, Las Vegas,

pp. 986-990.

[25] Schuett N., The Effects of Latency on

Ensemble Performance, Honors Thesis,

Stanford University, 2002.

[26] Akoumianakis D., Alexandraki C., Milios D.,

Nousias A., Synchronous Collaborative

Music Lessons and their digital materiality,

Proceedings of Web Audio Conference 2022

(WAC 2022), Cannes, 2022, DOI:

10.5281/zenodo.6768537.

[27] Makris D., Kaliakatsos-Papakostas M.,

Karydis I., Kermanidis K.L., Conditional

neural sequence learners for generating

drums’ rhythms, Neural Computing and

Applications, Vol. 31(6), 2019, pp. 1793-

1804, DOI: 10.1007/s00521-018-3708-6.

[28] Kritsis K., Kylafi T., Kaliakatsos-Papakostas

M., Pikrakis A., Katsouros V., On the

adaptability of recurrent neural networks for

real-time jazz improvisation accompaniment,

Frontiers in Artificial Intelligence, Vol. 3,

WSEAS TRANSACTIONS on ACOUSTICS and MUSIC

DOI: 10.37394/232019.2024.11.1

Spyros Brezas, Stella Paschalidou,

Chrisoula Alexandraki et al.

P-ISSN: 1109-9577

Volume 11, 2024

2021, 508727, DOI:

10.3389/frai.2020.508727.

[29] Kirkpatrick K., Can AI Demonstrate

Creativity?, Communications of the ACM,

Vol. 66(2), 2023, pp. 21-23, DOI:

10.1145/3575665.

[30] Fauconnier G., Turner M., The way we think:

Conceptual blending and the mind's hidden

complexities, Basic books, 2002.

[31] Zbikowski L.M., Conceptual blending,

creativity, and music, Music & Science, Vol.

22(1), 2018, pp. 6-23, DOI:

10.1177/1029864917712783.

[32] Goguen J.A., Harrell D.F., Style: A

Computational and Conceptual Blending-

Based Approach, The structure of style:

Algorithmic approaches to understanding

manner and meaning, Springer: Berlin, 2010,

DOI: 10.1007/978-3-642-12337-5_12.

[33] Zacharakis A., Kaliakatsos-Papakostas M.,

Kalaitzidou S., Cambouropoulos E.,

Evaluating human-computer co-creative

processes in music: a case study on the

CHAMELEON melodic harmonizer,

Frontiers in Psychology, Vol. 12, 2021,

603752, DOI: 10.3389/fpsyg.2021.603752.

[34] Kaliakatsos-Papakostas M., Velenis K.,

Pasias L., Alexandraki C., Cambouropoulos

E., An HMM-Based Approach for Cross-

Harmonization of Jazz Standards, Applied

Sciences, Vol. 13(3), 2023, 1338, DOI:

10.3390/app13031338.

[35] Bengio, Y., The consciousness prior. arXiv,

1709.08568, 2017, DOI:

10.48550/arXiv.1709.08568.

[36] HELMCA | Hellenic Electroacoustic Music

Composers Association, [Online].

https://www.essim.gr/index_en.html

(Accessed Date: November 1, 2023).

[37] Tamás Ungváry (1936 – 2024), [Online].

https://www.cime-icem.net/ (Accessed Date:

November 1, 2023).

[38] Emmerson S., Landy L., Expanding the

Horizon of Electroacoustic Music Analysis,

Cambridge University Press, 2016, DOI:

10.1017/CBO9781316339633.

[39] Roads C., Composing Εlectronic Μusic: Α

Νew Αesthetic, Oxford University Press,

2015.

[40] Valsamakis N., Aesthetics and Techniques in

the Electroacoustic Music of Iannis Xenakis,

Pella Publishing Company, 2009.

[41] Valsamakis N., Non-Standard Sound

Synthesis with Dynamic Models, PhD thesis,

University of Plymouth, 2013.

[42] Moore A., Sonic Art: An Introduction to

Electroacoustic Music Composition,

Routledge, 2016.

[43] Clarke M., Dufeu F., Manning P., Inside

Computer Music, Oxford University Press,

2020, DOI: 10.1017/S1355771823000043.

[44] Bovermann T., Campo A., Egermann H.,

Hardjowirogo S.-I., Weinzierl S., Eds.,

Musical instruments in the 21st century:

Identities, configurations, practices, Springer

Singapore, 2016, DOI: 10.1007/978-981-10-

2951-6.

[45] Patterson T., Instruments for New Music:

Sound, Technology and Modernism,

University of California Press, 2016.

[46] Wanderley M.M., Battier M., Eds., Trends in

Gestural Control of Music, IRCAM - Centre

Pompidou, 2000.

[47] Etmektsoglou I., Tzedaki K., Re-sounding

the message: Media aesthetic education

through the prisms of acoustic ecology and

psychology, Proceedings of the Global

Composition Conference on Sound, Media,

and the Environment, Darmstadt, 2012, pp.

19-23.

[48] Tzedaki K., Soundscape i

Kunsten/Soundscape Arts. Olso: NOTAM,

Organised Sound, Vol. 16, 2011, pp. 282-

283, DOI: 10.1017/S135577181100032X.

[49] Wrightson K., An Introduction to Acoustic

Ecology, Soundscape: Journal of Acoustic

Ecology, Vol. 1, 2000, pp. 10-13.

[50] Murray Schafer R., The Soundscape: Our

Sonic Environment and the Tuning of the

World, Destiny Books, 1993.

[51] Krause B., Farina A., Using ecoacoustic

methods to survey the impacts of climate

change on biodiversity, Biological

Conservation, Vol. 195, 2016, pp. 245–254,

DOI: 10.1016/j.biocon.2016.01.013.

[52] Gage S.H., Napoletano B.M., Cooper M.C.,

Assessment of ecosystem biodiversity by

acoustic diversity indices, Journal of the

Acoustical Society of America, Vol. 109(5

Suppl.), 2001, p. 2430, DOI:

10.1121/1.4744597.

[53] Pieretti N., Farina A., Morri D., A new

methodology to infer the singing activity of

an avian community: The Acoustic

Complexity Index (ACI), Ecological

Indicators, Vol. 11(3), 2011, pp.868–873,

DOI: 10.1016/j.ecolind.2010.11.005.

[54] Sueur J., Farina A., Gasc A., Pieretti N.,

Pavoine S., Acoustic Indices for Biodiversity

Assessment and Landscape Investigation,

WSEAS TRANSACTIONS on ACOUSTICS and MUSIC

DOI: 10.37394/232019.2024.11.1

Spyros Brezas, Stella Paschalidou,

Chrisoula Alexandraki et al.

P-ISSN: 1109-9577

Volume 11, 2024

Acta Acustica United With Acustica, Vol.

100(4), 2014, pp. 772-781, DOI:

10.3813/AAA.918757.

[55] Tzedaki K., Into the sounding environment A

compositional approach, De Montfort

University, 2011.

[56] Orphanos Y., Dimitriou V., Kaselouris E.,

Bakarezos E., Vainos N., Tatarakis M.,

Papadogiannis N.A., An integrated method

for material properties characterization based

on pulsed laser generated surface acoustic

waves. Microelectronic Engineering, Vol.

112, 2013, pp. 249-254, DOI:

10.1016/j.mee.2013.03.146.

[57] Κaleris K., Kaniolakis-Kaloudis E.,

Kaselouris E., Kosma K., Gagaoudakis E.,

Binas V., Petrakis S., Dimitriou V.,

Bakarezos M., Tatarakis M., Papadogiannis

N.A., Efficient ultrafast photoacoustic

transduction on Tantalum thin films, Applied

Physics A, Vol. 129, 2023, 527.

[58] Kifayat S., Shah H., Iqbal J., Ahmad P.,

Khandaker M.U., Haq S., Naeem M., Laser

induced breakdown spectroscopy methods

and applications: A comprehensive review,

Radiation Physics and Chemistry, Vol. 170,

2020, 108666, DOI:

10.1016/j.radphyschem.2019.108666.

[59] Jiang H., Qiu H., He N., Liao X., Research

on the optoacoustic communication system

for speech transmission by variable laser-

pulse repetition rates, Results in Physics,

Vol. 9, 2018, pp. 1291-1296, DOI:

10.1016/j.rinp.2018.04.050.

[60] Jones T.G., Hornstein M.K., Ting A.C.,

Wilkes Z.W., Intense underwater laser

acoustic source for Navy applications,

Journal of the Acoustical Society of America,

Vol. 125 (4 Suppl.), 2009, p. 2556, DOI:

10.1121/1.4783676.

[61] Kaleris Κ., Sound reproduction from laser-

driven pulsed acoustic sources

(Αναπαραγωγή ήχου μέσω παλμικών πηγών

οπτικής διέγερσης), PhD thesis, University

of Patras, 2021, DOI: 10.12681/eadd/50103.

[62] Kaleris K., Stelzner B., Hatziantoniou P.,

Trimis D., Mourjopoulos J., Laser-sound:

optoacoustic transduction from digital audio

streams, Scientific Reports, Vol. 11, 2021,

476, DOI: 10.1038/s41598-020-78990-z.

[63] Kaleris K., Stelzner B., Hatziantoniou P.,

Trimis D., Mourjopoulos J., Laser-Sound

Transduction from Digital ΣΔ Streams,

Journal of the Audio Engineering Society,

Vol. 70 (1/2), 2022, pp. 50-61, DOI:

10.17743/jaes.2021.0053.

[64] Fouilhe E., Goli G., Houssay A., Stoppani

G., Vibration modes of the cello tailpiece,

Archives of Acoustics, Vol. 36(4), 2011, pp.

713–726, DOI: 10.2478/v10168-011-0048-2.

[65] Duerinck T., Segers J., Skrodzka E.,

Verberkmoes G., Leman M., Van Paepegem

W., Kersemans M., Experimental

comparison of various excitation and

acquisition techniques for modal analysis of

violins, Applied Acoustics, Vol. 177, 2021,

107942, DOI:

10.1016/j.apacoust.2021.107942.

[66] Brezas S., Katsipis M., Orphanos Y.,

Kaselouris E., Kechrakos K., Kefaloyannis

N., Papadaki H., Sarantis-Karamesinis A.,

Petrakis S., Theodorakis I., Iliadis E.,

Karagkounidis T., Koumantos I., Tatarakis

M., Bakarezos M., Papadogiannis N.A.,

Dimitriou V., An Integrated Method for the

Vibroacoustic Evaluation of a Carbon Fiber

Bouzouki, Applied Sciences, Vol. 13(7),

2023, 4585, DOI: 10.3390/app13074585.

[67] Schedin S., Gren P.O., Rossing T.D.,

Transient wave response of a cymbal using

double-pulsed TV holography, Journal of the

Acoustical Society of America, Vol. 103(2),

1998, pp. 1217-1220, DOI:

10.1121/1.421254.

[68] Rossing T.D., Yoo J., Morrison A.,

Acoustics of percussion instruments: an

update, Acoustical Science and Technology,

Vol. 25(6), 2004, pp. 406-412, DOI:

10.1250/AST.25.406.

[69] Chatziioannou V., Reconstruction of an early

viola da gamba informed by physical

modeling, Journal of the Acoustical Society

of America, Vol. 145(6), 2019, pp. 3435–

3442, DOI: 10.1121/1.5111135.

[70] Gonzalez S., Salvi D., Baeza D., Antonacci

F., Sarti A., A data-driven approach to violin

making, Scientific Reports, Vol. 11, 2021,

9455, DOI: 10.1038/s41598-021-88931-z.

[71] Dalmont J.-P., Nederveen C.J., Joly N.,

Radiation impedance of tubes with different

flanges: Numerical and experimental

investigations, Journal of Sound and

Vibration, Vol. 244(3), 2001, pp. 505-534,

DOI: 10.1006/jsvi.2000.3487.

[72] Brezas S., Kaselouris E., Orphanos Y.,

Bakarezos M., Dimitriou V., Papadogiannis

N.A., Experimental and computational

vibroacoustic study of cymbals, Proceedings

WSEAS TRANSACTIONS on ACOUSTICS and MUSIC

DOI: 10.37394/232019.2024.11.1

Spyros Brezas, Stella Paschalidou,

Chrisoula Alexandraki et al.

P-ISSN: 1109-9577

Volume 11, 2024

of Forum Acusticum, Torino, 2023, pp. 1177-

1180.

[73] Torres J.A., Soto C.A., Torres-Torres D.,

Exploring design variations of the Titian

Stradivari violin using a finite element

model, Journal of the Acoustical Society of

America, Vol. 148(3), 2020, pp. 1496-1506,

DOI: 10.1121/10.0001952.

[74] Alexandraki C., Mimidis N., Viglis Y.,

Nousias A., Milios D., Tsioutas K.

Collaborative playalong practices in online

music lessons: the MusiCoLab Toolset.

Proceedings of the 4th International

Symposium on the Internet of Sounds, Pisa,

2023, DOI:

10.1109/IEEECONF59510.2023.10335236.

[75] Eppe M., Confalonieri R., Maclean E.,

Kaliakatsos M., Cambouropoulos E.,

Schorlemmer M., Codescu M., Kühnberger

K.-U., Computational invention of cadences

and chord progressions by conceptual chord-

blending, Proceedings of the Twenty-Fourth

International Joint Conference on Artificial

Intelligence, Buenos Aires, 2015, pp. 2445-

2451, DOI: 10.5555/2832581.2832590.

[76] Creative Blending of Harmonic Spaces ,

[Online].

https://ccm.web.auth.gr/blendedharmonisatio

ns.html (Accessed Date: November 1, 2023).

[77] Zhan Y., Liu C., Zhang F., Qiu Z.,

Experimental study and finite element

analysis based on equivalent load method for

laser ultrasonic measurement of elastic

constants, Ultrasonics, Vol. 69, 2016, pp.

243-247, DOI:10.1016/j.ultras.2016.03.014.

[78] Pandey P.K., Thareja R.K., Plume dynamics

of laser produced air plasma, Journal of

Physics: Conference Series, Vol. 208, 2010,

012091, DOI: 10.1088/1742-

6596/208/1/012091.

[79] Boratto T.H.A., Cury A.A., Goliatt L.,

Machine learning-based classification of

bronze alloy cymbals from microphone

captured data enhanced with feature selection

approaches, Expert Systems with

Applications, Vol. 215, 2023, 119378, DOI:

10.1016/j.eswa.2022.119378.

[80] Chhabra A., Singh A.V., Srivastava R.,

Mittal V., Drum Instrument Classification

Using Machine Learning, Proceedings of

2020 2nd International Conference on

Advances in Computing, Communication

Control and Networking (ICACCCN),

Greater Noida, 2020, DOI:

10.1109/ICACCCN51052.2020.9362963.

[81] Aravantinos-Zafiris N., Sigalas M.M.,

Katerelos D.T.G., Complete acoustic

bandgaps in a three-dimensional phononic

metamaterial with simple cubic arrangement,

Journal of Applied Physics, Vol. 133 (6),

2023, 065101, DOI: 10.1063/5.0127518.

[82] Gonzalez S., Salvi D., Baeza D., Antonacci

F., Sarti A., A data-driven approach to violin

making, Scientific Reports, Vol. 11, 2021,

9455, DOI: 10.1038/s41598-021-88931-z.

[83] Longo G., Gonzalez S., Antonacci F., Sarti

A., Predicting the acoustics of archtop

guitars using an AI-based algorithm trained

on FEM simulations, Proceedings of Forum

Acusticum, Torino, 2023, pp. 2965-2971.

Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

Vasilis Dimitriou, Maximos Kaliakatsos-

Papakostas, and Evaggelos Kaselouris carried out

the conceptualization.

Spyros Brezas, Stella Paschalidou, Chrisoula

Alexandraki, Christine Georgatou, Konstantinos

Kaleris, Maximos Kaliakatsos-Papakostas,

Emmanouil Kaniolakis-Kaloudis, Evaggelos

Kaselouris, Yannis Orphanos, Helen Papadaki,

Katerina Tzedaki, and Nikolas Valsamakis

implemented writing and original draft preparation.

Stella Paschalidou, Chrisoula Alexandraki, Makis

Bakarezos, Vasilis Dimitriou, Maximos

Kaliakatsos-Papakostas, Evaggelos Kaselouris,

Nektarios A Papadogiannis, Katerina Tzedaki, and

Nikolas Valsamakis were responsible for writing,

review and editing.

Spyros Brezas was responsible for the management

and coordination responsibility for the research

activity planning and execution and is the

corresponding author (denoted by *).

All authors have read and agreed to the published

version of the manuscript.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

No funding was received for conducting this study.

Conflict of Interest

The authors have no conflicts of interest to declare.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

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DOI: 10.37394/232019.2024.11.1

Spyros Brezas, Stella Paschalidou,

Chrisoula Alexandraki et al.

P-ISSN: 1109-9577

Volume 11, 2024