Troubadour: Inverse Dictation Games for Ear Training

Klara

Znider

, Matija Podbreznik

Ziga Klun

, Peter

Savli

2 a

Matija Marolt

1 b

and Matev

z Pesek

1 c

Faculty of Computer and Information Science, University of Ljubljana, Slovenia

Conservatory of Music and Ballet Ljubljana, Slovenia

Keywords:

Music Theory, E-Learning, Gamiﬁcation, Sight-Singing, Ear-Training.

Abstract:

This study evaluates the integration of inverse melodic and rhythmic dictation exercises into the gamiﬁed e-

learning Troubadour platform. The platform offers gamiﬁed and personalised applications for different types

of ear and music theory training. The platform was developed as a complementary tool to support music

theory classes with automated ear-training exercises with automatic generation and evaluation. To further

extend its usability, we present two ear-training apps, which invert the standard ear-training techniques of

listening and writing down the solution. The inverse melodic and rhythmic dictation exercises now offer voice

or instrument user input as a user response to the written melodic or rhythmic prompt. In this paper, we

gathered user feedback using UEQ and Meega+ questionnaires, which showed positive results, particularly in

terms of novelty, perspicuity, and efﬁciency. Notably, gamiﬁcation elements such as achievement badges and

leaderboards have been integrated in line with other exercises on the platform and contributed to a dynamic

and engaging learning experience. These results highlight the potential of inversion games in music education

and emphasise the importance of gamiﬁed platforms in enhancing the overall learning experience.

1 INTRODUCTION

In the constantly evolving ﬁeld of education, techno-

logical progress is playing an increasingly important

role and is leading to innovative educational tools.

The integration of e-learning approaches into curric-

ula already demonstrated positive effects in various

areas of education. In addition, researchers suggested

that incorporating gamiﬁed elements into e-learning

processes can beneﬁt students by making tasks more

engaging and motivating (Saleem et al., 2022).

Within the domain of music education, there are

several aspects that need to be considered by de-

velopers of teaching methods. While e-learning of

music theory through written user input has been in

use for some time, the important aspect of music is

also its auditory nature. The task of training the ear

for rhythm and melody can be accomplished in two

ways—through auditory recognition and by perform-

ing given exercises. While neither is trivial, the lat-

ter presents developers with additional challenges in

terms of recognizing the auditory input.

https://orcid.org/0000-0002-8097-7983

https://orcid.org/0000-0002-0619-8789

https://orcid.org/0000-0001-9101-0471

In response to these challenges, we propose

an extension to our existing e-learning platform

Troubadour, tailored for music theory exercises. This

extension introduces inversion games, i.e. inverse

rhythmic and melodic dictation training that seam-

lessly integrates with Troubadour’s existing exercises

and gamiﬁcation elements. To evaluate the effective-

ness and user experience of the proposed exercises,

we conducted user studies. The positive feedback

from participants in terms of engagement, usability,

and overall experience speaks to the effectiveness of

the proposed work. Therefore, this research con-

tributes to the advancement of music education tech-

nology and emphasises the importance of consider-

ing the auditory dimensions of music learning through

gamiﬁed e-learning platforms.

The following section reviews related work and

describes the Troubadour platform. Details of the

proposed extension, the challenges encountered and

the experimental setup are presented in Section 3.

Section 4 presents comprehensive evaluation results

that shed light on the potential impact on music

education. Conclusions and future work are presented

in Section 5.

606

Žniderši

c, K., Podbreznik, M., Klun, Ž., Šavli, P., Marolt, M. and Pesek, M.

Troubadour: Inverse Dictation Games for Ear Training.

DOI: 10.5220/0012747200003693

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 16th International Conference on Computer Supported Education (CSEDU 2024) - Volume 1, pages 606-613

ISBN: 978-989-758-697-2; ISSN: 2184-5026

2 RELATED WORK

2.1 E-Learning and Gamiﬁcation

Within the ever-evolving educational landscape, the

merging of technology and pedagogy has led to in-

novative paradigms, particularly in the areas of e-

learning and gamiﬁcation. As educational environ-

ments increasingly shift to virtual platforms, research

into effective learning methods and the incorpora-

tion of game elements, known as gamiﬁcation, has

proven to be a compelling strategy to engage and mo-

tivate learners in digital spaces. The majority of re-

search and evaluations have focused predominantly

on higher levels of education (Woody, 2021; Urh

et al., 2015; De Sousa Borges et al., 2014; Kyewski

and Kr

amer, 2018), and covered different areas such

as music (Biasutti et al., 2023; Woody, 2021; Wagner,

2017), medicine (Morton et al., 2016), database man-

agement (Hassan et al., 2019), or a range of subjects

(Aparicio et al., 2019; Kyewski and Kr

amer, 2018).

Most studies report on the positive aspects of using

modern technologies, such as ﬂexibility, accessibil-

ity, self-paced learning, personalization and instant

feedback. In addition, some studies focused on the

challenges that arise when introducing e-learning (Bi-

asutti et al., 2023; Martinez-Garcia et al., 2023). Bia-

sutti et al. showed that the transition to distance learn-

ing in Italian primary school during the COVID-19

pandemic encountered obstacles due to the sudden-

ness of pedagogical changes and teachers’ insufﬁcient

technological skills. Teachers also expressed con-

cerns about the detrimental effects of distance learn-

ing on many students, especially younger students.

However, positive outcomes were reported in terms of

familiarisation with information and communication

technologies (ICT), and it was suggested that more

deliberate and supported implementation could allow

for a smoother and better transition. Furthermore,

Martinez-Garcia et al. present both the opportunities

and challenges associated with the integration of arti-

ﬁcial intelligence into e-learning.

Gamiﬁcation is deﬁned as the use of game ele-

ments and mechanics in non-game contexts (Deter-

ding et al., 2011), however the line between games

and gamiﬁed applications is often blurred. In their

literature review on gamiﬁcation, Behl et al. (2022)

identiﬁed four main themes—personalization, learn-

ing styles, learner engagement and game elements.

The most commonly used game elements, as de-

scribed by Antonaci et al. (2019), are badges, leader-

boards, points, feedback, challenges, likes/social fea-

tures, communication channels, narratives, levels,

progress bars, teams, agents, medals, avatar, tro-

phies, time limit, task, virtual currency, personal-

ization features, mission, replayability, goal indica-

tors, competition, win status. In a survey of pri-

mary school children survey and a literature review

by Nand et al. (2019), graphics, feedback and chal-

lenge were identiﬁed as the most attractive features of

computer games. While Kyewski and Kr

amer (2018)

could not prove any signiﬁcant improvement through

the use of gamiﬁcation and grades or quiz results

were not inﬂuenced by badges, such game elements

do not hinder the increase in learning outcomes, fos-

tering activity, and motivation. Furthermore, De Fre-

itas (2018) shows that games are an effective learn-

ing tool and widespread adoption is only a matter

of time. It is therefore becoming increasingly im-

portant to harmonise the different disciplinary per-

spectives, address methodological challenges and

develop a common terminology. In research on mas-

sive open online courses (MOOCs), gamiﬁcation has

been found to play a central role in success, as it posi-

tively inﬂuences several aspects of the courses (usage,

individual impact and organisational impact) (Apari-

cio et al., 2019). An advanced approach to gamiﬁca-

tion is the personalization of the learning application.

Hassan et al. developed a system that provides stu-

dents with different gamiﬁcation elements depending

on their learning type. The results showed that adap-

tive gamiﬁcation elements and activities selected ac-

cording to learners’ learning dimensions signiﬁcantly

increased factors such as motivation, course comple-

tion, interest, and user interaction in the e-learning

course (Hassan et al., 2019).

2.2 Sight Reading and Sight Singing

In the area of music education, Sch

uler (2021) ad-

dresses the challenges faced by instrumentalists and

vocalists by highlighting their respective weaknesses

in singing-related exercises and ear training tasks.

To counteract student “un-motivation,” Sch

uler ad-

vocates integrating technological tools like online

karaoke and platforms such as SmartMusic, which

lead to higher student motivation, better performance

in less time, improved audiation skills, and improved

solfege skills compared to traditional singing exer-

cises. The concept of audiation, which is crucial to

improving sight-singing skills, is at the heart of these

efforts. Research shows that incorporating famil-

iar music choices and feedback mechanisms through

platforms like SmartMusic, which provide visual and

audio feedback, signiﬁcantly increases student moti-

vation and performance. In addition, Sch

uler high-

lights the effectiveness of various software tools such

as EarMaster Pro, MacGamut, Practica Musica, Au-

Troubadour: Inverse Dictation Games for Ear Training

607

ralia, Teoria.com and EarTrainer, the latter of which

allows teachers to create and assess melodic, rhyth-

mic and harmonic dictations.

In recent years, numerous applications have

been developed to improve sight-singing and sight-

learning. Ella

, a sight-singing ear-training app,

stands out by offering engaging exercises that include

gamiﬁed elements such as leaderboards and scores,

paired with pitch evaluation and detailed analysis and

feedback on the user’s performance. ABRSM (Asso-

ciated Board of the royal Schools of Music)

offers

various music trainer apps with gamiﬁed elements,

covering piano and violin scales, sight-reading, mu-

sic theory, and aural training, the latter being used to

improve sight-singing. Many exercises are automat-

ically marked, and there are also tools for reviewing

and evaluating performance. Perfect Ear

offers vari-

ous exercises for ear training, rhythm and melody dic-

tation, sight-reading, absolute pitch, and note singing,

which are suitable for self-learning as they do not al-

low monitoring by a teacher. MyEarTraining

has

proven to be helpful in increasing success in the

“Western Music Theory and Ear Training” course

(Sezer and Temiz, 2023). It offers singing exercises,

progress tracking, cross-device synchronization, and

customizable exercise generation. Sight Reading Fac-

tory

Pavlovi

c (2018) supports the user with cus-

tomizable sight-reading exercises for instruments and

vocals. The program is particularly useful for schools

as it provides sight-reading exercises for full ensem-

ble, but it lacks the function of automatic feedback for

the user. Feedback can only be given by the teacher

via the assignment function. The improvement of

sight-reading skills is also addressed in ToneGym

iClef (different clef positions on the staff) (Barat

et al., 2023), and Adventure in Music Land (for chil-

dren aged 9-13). The apps are mostly available as web

apps or in the iOS and Google Play stores.

2.3 Troubadour

In recent years, Pesek et al. have been developing

Troubadour, a gamiﬁed e-learning platform tailored

for ear training (Pesek et al., 2020a,b, 2022). This

web-based open-source

application is speciﬁcally

geared towards learning music theory and aims to en-

https://ellaapp.io

https://www.abrsm.org

https://www.perfectear.app

https://www.myeartraining.net

https://www.sightreadingfactory.com

https://www.tonegym.co

https://bitbucket.org/ul-fri-lgm/troubadour backend

https://bitbucket.org/ul-fri-lgm/troubadour ﬂutter

gage students in a dynamic and personalized learn-

ing experience through a series of exercises. The ex-

ercises on the platform cover various aspects includ-

ing melodic and interval dictation, rhythmic dictation,

and harmony exercises. Troubadour was developed

in collaboration with a music conservatory and suc-

cessfully meets the needs of both teachers and stu-

dents. It addresses teachers’ concerns about techni-

cal skills by providing a non-complex user interface

while meeting students’ needs for motivation through

gamiﬁcation elements. The platform offers features

such as user administration and tracking, automatic

exercise generation, and difﬁculty levels aligned with

the conservatory curriculum. Troubadour has been

integrated into music theory courses and has received

positive feedback from students for its user-friendly

interface and positive impact on students’ exam re-

sults. Troubadour stands out as an easily expand-

able tool that is accessible across all major platforms.

Most notably, the application utilizes gamiﬁcation el-

ements such as user proﬁles, avatar creation, achieve-

ment badges, progression levels, and leaderboards to

provide an engaging and rewarding learning environ-

ment.

Troubadour was published as a mobile application

for both Android

and iOS

platforms, along with the

recently re-designed user interface (Fig. 1). The plat-

form offers a mobile-ﬁrst user experience for existing

rhythmic, interval and harmony games available both

as mobile app and web interface

3 INVERSION GAMES

As an extension of the existing Troubadour app, we

propose the integration of inversion games to help im-

prove students’ sight-singing skills. The introduction

of new features, in particular inverse rhythmic and

melodic dictation training, ﬁts seamlessly into the ex-

isting exercises and gamiﬁcation elements of the app.

While the existing regular rhythmic and melodic dic-

tation tasks ask users to transcribe the audio exam-

ples, the proposed inverse dictations involve perform-

ing a written rhythmic or melodic pattern. During

the development process, however, challenges arose

in the processing of the auditory input.

In the inverse rhythmic dictation exercises, users

are given a written example of a rhythmic pattern and

are required to record the rhythm by singing it into the

https://play.google.com/store/apps/details?id=si.

trubadur.v2

https://apps.apple.com/si/app/trubadur-si/

id6449623053

Available at: https://trubadur.si

CSME 2024 - 5th International Special Session on Computer Supported Music Education

608

(a) Games view. (b) In-game progress view.

Figure 1: Troubadour mobile interface.

device’s microphone. Once they have conﬁrmed their

recording, they automatically receive feedback. The

app processes the provided audio signal with a binary

representation for each detected note and compares

it with the binary reference representation calculated

based on the original pattern. Tolerances are set for

the synchronization and comparison of the two binary

patterns, including the start time tolerance (which al-

lows shifts in the start index of the dictated note) and

the duration tolerance (which allows the duration of

the dictated note to be extended or shortened by a

maximum of 25% compared to the length of the cor-

responding note in the generated signal).

Similarly, in inverse melodic and harmonic dicta-

tion exercises, the user is provided with melodic pat-

tern or arpeggiated chords. Based on the given refer-

ence note, the user records the pattern and submits the

recording for evaluation. Flutter audio capture library

was used during the recording process due to its capa-

bility for real-time audio capturing. The information

about the frequencies is obtained by pitch detection li-

braries, based on the Yin algorithmm (De Cheveigne

and Kawahara, 2002), and each note is represented as

a list of values that takes into account the recognized

frequencies in the human voice. The predominant fre-

quency is converted into a musical notation and com-

pared with the notation of the reference pattern. Ini-

tially, the recording was depicted using musical notes

to represent both the user input and the reference pat-

tern. Subsequently, we decided to enhance the visu-

alization by incorporating a piano roll alongside the

existing musical note representation with clear differ-

entiation between correct and incorrect notes. Fur-

thermore, users were provided with the ﬂexibility to

adjust the octave of the pattern to better align with

their vocal range.

During the last six years of the Troubadour plat-

form development, the gamiﬁed e-learning platform

has continued to uphold its commitment to provid-

ing a dynamic and engaging learning experience. No-

tably, the gamiﬁcation elements, including achieve-

ment badges, progress levels, and performance ratings

on leaderboards, are also included in the newly devel-

oped exercises. The incorporation of these elements

not only motivates students but also adds an element

of healthy competition, enhancing the overall educa-

tional experience within the Troubadour ecosystem.

Figure 2: Three versions of the inverse interval dictation

game. The ﬁnal version contains the piano roll as a real-

time visual aid, showing the sang pitch in a form of a blue

box (left part of the screen).

3.1 Experimental Design

To collect feedback and assess the overall user expe-

rience, we conducted a two-part survey, one for each

tool. First, participants were asked to complete a de-

mographic questionnaire, in which they provided in-

formation about their gender, age, the instrument they

play, years of formal instruction, and years of music

school attendance.

3.1.1 User Experience Questionnaire

The user experience with the newly introduced tools

was evaluated using the User Experience Question-

naire (UEQ). This questionnaire covers aspects of

both pragmatic (goal-oriented) and hedonic (non-

goal-oriented) quality and consists of 26 items cat-

egorized into six scales: Attractiveness, Perspicuity,

Efﬁciency, Dependability, Stimulation, and Novelty

(Laugwitz et al., 2008). Each scale item consists of

Troubadour: Inverse Dictation Games for Ear Training

609

two terms with opposite meanings, with half of the

items on a scale beginning with the positive term and

the other half of the items beginning with the negative

term. The provided analysis tools are tailored to ac-

commodate this term order. Participants ranked each

item on a 7-point Likert scale (Schrepp, 2015). Users

were provided with the ofﬁcial Slovenian translation

of the UEQ to ensure accurate responses.

Table 1: 26 items of the 6 scales of the UEQ as they were

presented to the users(Schrepp et al., 2014).

Scales Pairs of items

Attractiveness annoying enjoyable

(pure valence) good bad

unlikable pleasing

unpleasant pleasant

attractive unattractive

friendly unfriendly

Perspicuity not understandable understandable

(pragmatic) easy to learn difﬁcult to learn

complicated easy

clear confusing

Efﬁciency fast slow

(pragmatic) inefﬁcient efﬁcient

impractical practical

organized cluttered

Dependability unpredictable predictable

(pragmatic) obstructive supportive

secure not secure

meets expectations does not meet expectations

Stimulation valuable inferior

(hedonic) boring exiting

not interesting interesting

motivating demotivating

Novelty creative dull

(hedonic) inventive conventional

usual leading edge

conservative innovative

The UEQ handbook (Schrepp, 2015) provides the

following interpretation of the results:

• Bad: In the range of the 25% worst results.

• Below Average: 50% of the results in the bench-

mark are better than the result for the evaluated

product, 25% of the results are worse.

• Above Average: 25% of the results in the bench-

mark are better than the result for the evaluated

product, 50% of the results are worse.

• Good: 10% of the results in the benchmark data

set are better and 75% of the results are worse.

• Excellent: In the range of the 10% best results.

3.1.2 Meega+ Questionnaire

The inverse melodic and harmonic dictation tool was

further evaluated using an adapted Meega+ (Model

for the Evaluation of Educational Games) question-

naire, which was developed for the systematic assess-

ment of the quality of educational games in computer

education from a student’s standpoint (Petri et al.,

2018). This model comprises two primary quality

factors—game experience and usability—and their

respective dimensions. To streamline the evaluation

and avoid redundancy with UEQ metrics, we selected

seven speciﬁc metrics:

• I had fun with the game.

• This game is appropriately challenging for me.

• The game contributed to my learning in this

course.

• The game design is attractive (interface, graphics,

cards, boards, etc.).

• I would recommend this game to my colleagues.

• The game rules are clear and easy to understand.

• When I make a mistake, it is easy to recover from

it quickly.

The answers were recorded on a 5-point Likert

scale, with 1 being ”strongly disagree” and 5 being

”strongly agree” In addition, open-ended questions

were used to get feedback on what users liked, sug-

gestions for improvement, and additional comments.

4 RESULTS

The ﬁrst experimental group focused on the new in-

verse rhythmic dictation tool and consisted of 15 par-

ticipants. The majority of the participants were male

and accounted for 73.3% of the participants, while

the proportion of female participants was 26.7%. The

age distribution showed that the vast majority, 66.7%,

were up to 25 years old. 20% fell into the age group

of 26 to 40 years and 13.3% into the age group of

41 to 60 years. No participant was over 61 years

old. The group consisted predominantly of musi-

cians (86.7%), while the proportion of music teach-

ers was lower (13.3%). A signiﬁcant proportion of

participants demonstrated proﬁciency in playing mul-

tiple instruments: 33.3% playing two instruments and

26.7% playing three instruments. In terms of musical

instrument proﬁciency, the majority of participants

demonstrated proﬁciency in playing the piano, con-

stituting 46.7%, followed by guitar and singing, both

at 33.3%. Other instruments included drums, accor-

dion, trumpet, euphonium, double bass, French horn,

and tuba. The duration of formal learning varied, with

1 participant having no formal learning, 1 participant

with 1 year, 6 participants with 6 to 9 years, and 7

participants with 10 or more years.

The second experimental group, focusing on the

inverse melodic and harmonic dictation, was more

complex. The experiment lasted for two weeks and

CSME 2024 - 5th International Special Session on Computer Supported Music Education

610

included 7 participants in the ﬁrst week and 8 in the

second week, with 5 participants taking part in both

weeks. After the ﬁrst week of evaluation, the tool was

improved based on user feedback. All participants

were ﬁrst-year students at the Ljubljana Conservatory

of Music and Ballet. The majority of the participants

were male and made up 86.7% of the total. The av-

erage age of the participants was 17.47 years, with an

average of 7.8 years of playing an instrument and 6.3

years attending music school. The most common in-

strument among the participants was the saxophone,

which was played by four people. Mobile phone plat-

forms were varied, with 8 participants using Android

and 7 using iOS.

The results of the UEQ questionnaires show pre-

dominantly positive feedback from the participants

involved in the research. The creators of the question-

naire’s highlight that, given the calculation of means

across different people with different opinions and re-

sponse tendencies, values above +2 or below -2 are

extremely unlikely to occur (Schrepp, 2015). As ex-

pected, the initial assessment of the inverse melodic

dictation resulted in lower scores in the ﬁrst week,

which improved in the subsequent assessment.

Figure 3: UEQ results of both groups in Week 2 of melodic

tool analysis and rhythmic tool evaluation group.

Figure 4: Comparison of UEQ results between both groups

in Week 2 of melodic tool analysis.

In the ﬁrst group, participants rated each scale

higher in the second evaluation, with the exception

of Stimulation. Conversely, the second group, being

Figure 5: UEQ results of rhythmic tool analysis.

Figure 6: Results of Meega+ questionaire.

unfamiliar with the previous version, rated each scale

higher than the ﬁrst group, with the exception of De-

pendability. A visual representation of this compari-

son can be found in Figure 4. Compared to the eval-

uation of the inverse rhythm dictation tool evaluation,

the only higher scores were recorded on the Efﬁciency

scale. The lowest overall score received the Novelty

scale in the ﬁrst week of the evaluation. In addition,

pragmatic quality scales received higher scores than

hedonic quality scales, as depicted in Figure 3.

In line with the benchmarks for interpreting the

UEQ results (Schrepp, 2015), the rating ”Excellent”

was awarded for Novelty and Perspicuity in Group 2,

and Efﬁciency in Group R. Attractiveness was rated

”Good”, while Dependability and Stimulation were

classiﬁed as ”Above Average” in Group 2 (also in

Group R).

The results of a part of the Meega+ questionnaire

(see ﬁgure 6) were also positive, with all average

scores above the mean of the 1-5 scale. It is worth

noting that the only category that did not achieve an

average score above 4 is related to error management

and the ability to correct errors. These results con-

tribute to an overall positive assessment of the user

experience, as they indicate a high level of satisfac-

tion with various aspects.

4.1 Suggestions for Improvement

Suggested improvements include increasing the sen-

sitivity of the application, implementing more precise

audio recognition to increase the accuracy of the ex-

Troubadour: Inverse Dictation Games for Ear Training

611

ercises, more customization options in terms of ad-

justing the level of difﬁculty and tolerance for de-

viations from the ideal rhythm, the ability to dis-

able the visual metronome, integrating an audible

metronome, emphasizing the importance of correct

rhythm notation, recognizing clapping, and adding

polyrhythm exercises. Other suggested improvements

include smoother operation, correction of errors in ex-

ercise generation, and implementation of the natural

(

) sign.

4.2 Educators’ Opinion

The evaluation of the inverse rhythm dictation by two

music teachers provides valuable information on its

practical implementation in music lessons. The ﬁrst

teacher emphasizes the importance of cultivating a

natural sense of rhythm and an emotional connection

to music and points out that the application may not

be suitable for younger students as it is geared to-

wards self-directed learning. In contrast, the second

educator sees signiﬁcant potential in the application,

especially if it includes more complex rhythms and

features. This educator can envision the functionality

beneﬁting musicians of all levels, provided it meets

accurate notation standards.

5 CONCLUSION AND FUTURE

WORK

The results of the study show overall positive feed-

back from participants who used the newly developed

inverse rhythmic and melodic dictation tools.

Regarding the inverse melodic dictation, the initial

lower scores in the ﬁrst week improved in the subse-

quent evaluation, which was in line with expectations.

The second group, who were unfamiliar with the pre-

vious version, generally rated each scale higher than

the ﬁrst group, indicating positive acceptance. The

Meega+ questionnaire further underpinned the posi-

tive results. However, it is worth noting that the cat-

egory relating to error management and the ability to

correct errors received a score of slightly less than 4,

suggesting that there is still room for improvement in

this particular aspect. More speciﬁcally, the user ex-

perience could be further improved by taking into ac-

count the speciﬁc suggestions of students and teach-

ers.

Overall, the positive feedback and constructive

suggestions indicate that the tools can effectively sup-

port music education and self-study. The inclusion of

gamiﬁcation elements, as seen in the Troubadour plat-

form, adds an engaging and motivating layer to the

learning experience.

Inverse melodic dictation exercises have already

been integrated into the production app and are be-

coming increasingly popular with students and teach-

ers. In the upcoming update of the Troubadour plat-

form, we plan to introduce inverse rhythm dictation

exercises, which were evaluated in the present study.

ACKNOWLEDGEMENTS

This research was conducted as part of the basic re-

search project Music for the young people since 1945

and Jeunesses Musicales in Slovenia [project number

J6-3135], funded by the Slovenian Research and In-

novation Agency from the state budget.

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