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Michele Della Ventura

Pianoforte
Teoria, Analisi e Composizione
Tecnologie Musicali
Informatica Musicale
Michele Della Ventura

Michele Della Ventura è esperto di Tecnologie Musicali, ricercatore e instructional designer, con un’attività scientifica internazionale incentrata sull’intelligenza artificiale applicata alla musica e ai processi di apprendimento, tra sistemi intelligenti, analisi simbolica del testo musicale e technology-enhanced learning. Si è diplomato brillantemente in pianoforte sotto la guida di Francesco Bencivenga e ha proseguito gli studi di composizione nella scuola di Bruno Coltro, approfondendo inoltre la musica contemporanea sotto la guida di Bruno Bettinelli a Milano. Durante questo periodo ha partecipato e vinto diversi concorsi nazionali e internazionali di esecuzione musicale, esperienze che hanno posto le basi per una intensa attività concertistica.
Parallelamente agli studi musicali, si è laureato in Discipline Tecnologiche con lode, ottenendo una borsa di studio. Presso l’Università di Roma Tor Vergata ha proseguito il proprio percorso formativo conseguendo due Master universitari di II livello, nei quali ha approfondito i modelli di e-learning e l’uso avanzato delle tecnologie digitali, con particolare attenzione alle applicazioni dell’intelligenza artificiale in ambito educativo e in contesti applicativi più ampi.
Attualmente, oltre all’insegnamento del pianoforte, è Professore di Tecnologie per l’Informatica e Musicali presso Accademie di Belle Arti e Conservatori di Musica. In passato ha collaborato con università e istituzioni nazionali e internazionali come docente e membro di team scientifici, contribuendo a progetti su Big Data, tecnologie semantiche e analisi musicale computazionale, con particolare attenzione all’indicizzazione del testo musicale simbolico.
Ha tenuto keynote e interventi in oltre 25 paesi, tra cui Italia, Austria, Canada, Cina, Repubblica Ceca, Estonia, Francia, Germania, Grecia, Hong Kong, Ungheria, Irlanda, Giappone, Norvegia, Polonia, Portogallo, Romania, Singapore, Spagna, Regno Unito e Stati Uniti (Baltimora, Boston, Las Vegas, New York, Washington).
È autore di numerose pubblicazioni scientifiche internazionali, con ricerche focalizzate su intelligenza artificiale, sistemi intelligenti per l’apprendimento e metodi di apprendimento innovativi. Ha curato pubblicazioni come editore, contribuendo alla diffusione internazionale di studi e metodologie avanzate nel campo della musica e dell’apprendimento supportato dalla tecnologia.



Michele Della Ventura is an expert in Music Technologies, researcher, and instructional designer, with an international scientific activity focused on artificial intelligence applied to music and learning processes, including intelligent systems, symbolic music text analysis, and technology-enhanced learning. He graduated with honors in piano under the guidance of Francesco Bencivenga and continued his studies in composition at Bruno Coltro's school, also deepening his knowledge of contemporary music under the guidance of Bruno Bettinelli in Milan. During this period, he participated in and won several national and international music competitions, experiences that laid the foundations for an intense concert career.
Alongside his musical studies, he graduated with honor in Technological Disciplines with distinction, receiving a scholarship. At the University of Rome Tor Vergata, he continued his academic education, by completing two second-level Master’s degrees, where he focused on e-learning models and the advanced use of digital technologies, with particular attention to artificial intelligence applications in education and broader applied contexts.
Currently, in addition to teaching piano, he is a Professor of Musical and Information Technology at Academies of Fine Arts and Music Conservatories. In the past, he collaborated with national and international universities and institutions as lecturer and member of scientific team, contributing to projects on Big Data, semantic technologies, and computational music analysis, with a particular focus on symbolic music text indexing.
He has delivered keynotes and talks in over 25 countries, including Italy, Austria, Canada, China, Czech Republic, Estonia, France, Germany, Greece, Hong Kong, Hungary, Ireland, Japan, Norway, Poland, Portugal, Romania, Singapore, Spain, the United Kingdom, and the United States (Baltimore, Boston, Las Vegas, New York, Washington).
He is the author of numerous international scientific publications, with research focused on artificial intelligence, intelligent learning systems, and innovative learning methods. He has also edited publications, contributing to the international dissemination of advanced studies and methodologies in music and technology-supported learning.


Pubblicazioni/ Publications

Journals and Books

M. Della Ventura, I. Bordignon (2022). Towards an Ecocritical Norwegian Folklore and Music: Water, Love and Death. IJMSTA. 2022 April 18; 3 (2): 17-31.
ISSN: 2612-2146, DOI: https://doi.org/10.48293/IJMSTA-83

M. Della Ventura (2021). From the Music Learning Process to Its Effective Design, International Journal of Emerging Technologies in Learning (iJET), Vol. 16 No. 21, pp. 13-25, November 2021.
ISSN: 1863-0383, DOI: https://doi.org/10.3991/ijet.v16i21.24273

M. Della Ventura, I. Bordignon (2021). Ecocritical Musicology as a Key to Understanding Grieg's Music. IJMSTA. 2021 November 19; 3 (2): 32-43.
ISSN: 2612-2146, DOI: https://doi.org/10.48293/IJMSTA-79

M. Della Ventura (2018). DNA Musicale: matematicamente suono, ABEditore, Milano. ISBN: 978-88-6551-281-4

E.E. Mahmut, M. Della Ventura, V. Stoicutivadar (2018), An Entropy-Based Computer Model for the Measurement of Phonetic Similarity: Dyslalia Screening in Early School-Age Children, Applied Medical Informatics, Vol. 40, No. 1-2 /2018, pp. 15-23.

M. Della Ventura (2017). Creating Inspiring Learning Environments by means of Digital Technologies: A Case Study of the Effectiveness of WhatsApp in Music Education, EAI Endorsed Transactions on e-Learning (Journal), Vol. 4, pp. 1-9, July 2017. ISSN: 2032-9253, DOI: http://dx.doi.org/10.4108/eai.26-7-2017.152906

M. Della Ventura (2017). Digital Technologies and Digital Strategies to Enhance Musical Knowledge: A Qualitative Case Study , The International Journal of E-Learning and Educational Technologies in the Digital Media (IJEETDM), Vol. 3(2), pp. 75-82, June 2017.
ISSN: 2410-0439, DOI: http://dx.doi.org/10.17781/P002329

M. Della Ventura (2017). Dai numeri al suono, ABEditore, Milano, ISBN: 978-88-6551-252-4

M. Della Ventura (2014). Discovering Hidden Themes in Symbolic Music Text, International Journal on Lecture Notes on Software Engineering, Vol. 3(3), pp. 210-213, August 2015.
ISSN: 2301-3559, DOI: 10.7763/LNSE.2015.V3.192

M. Della Ventura (2014). The Social Network as a Filter for Internet Research , International Journal of Information and Education Technology, IPEDR Vol. 70(1), pp. 1-6.
ISSN: 2010-4626, DOI: 10.7763/IPEDR.2014.V70.1

M. Della Ventura (2014). Problem-Based Learning and e-Learning in Sound Recording, International Journal of Information and Education Technology, Vol. 4(5), pp. 426-429.
ISSN: 2010-3689, DOI: 10.7763/IJIET.2014.V4.443

M. Della Ventura (2014). Detection of Historical Period in Symbolic Music Text, International Journal of e-Education, e-Business, e-Management and e-Learning, Vol.4(1), pp. 32-36, February 2014.
ISSN: 2010-3654, DOI: 10.7763/IJEEEE.2014.V4.297

M. Della Ventura (2013). Detection of Historical Period in Symbolic Music Text, International Journal of e-Education, e-Business, e-Management and e-Learning, Vol. 4(1), pp. 32-36.
ISSN: 2010-3654, DOI: 10.7763/IJEEEE.2014.V4.297

M. Della Ventura (2013). Relations between melody and rhythm on music analysis: representations and algorithms for Symbolic Musical Data, International Journal of Applied Physics and Mathematics, Vol. 3(2), pp. 87-91.
ISSN: 2010-362X, DOI: 10.7763/IJAPM.2013.V3.181

M. Della Ventura (2012). Teoria e pratica della ripresa stereofonica, ABEditore, Milano. ISBN: 978-88-6551-111-4


Conferences

Della Ventura, M (2025). Automatic Correction of Symbolic Musical Text During Optical Music Recognition. In: Daimi, K., Alsadoon, A. (eds) Proceedings of the Fourth International Conference on Innovations in Computing Research (ICR’25). ICR 25 2025. Lecture Notes in Networks and Systems, vol 1487. Springer, Cham.
ISBN: 978-3-031-95651-5, DOI: https://doi.org/10.1007/978-3-031-95652-2_9

Della Ventura, M (2025). Generative Artificial Intelligence as a Tool to Design the Learning Process Using Active Learning Methodologies. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the Third International Conference on Advances in Computing Research (ACR’25). ACR 25 2025. Lecture Notes in Networks and Systems, vol 1346. Springer, Cham.
ISBN: 978-3-031-87646-2, DOI: https://doi.org/10.1007/978-3-031-87647-9_7

Della Ventura, M. (2024). Self-adaptive Learning Algorithm as a Tool for the Development and Strengthening of the Dyslexic Student's Skills in the Study of Musical Composition. In: Fortino, G., Kumar, A., Swaroop, A., Shukla, P. (eds) Proceedings of Third International Conference on Computing and Communication Networks. ICCCN 2023. Lecture Notes in Networks and Systems, vol 917. Springer, Singapore.
ISBN: 978-981-97-0891-8, DOI: https://doi.org/10.1007/978-981-97-0892-5_33

Della Ventura, M. (2024). A Statistical Approach for Modeling the Expressiveness of Symbolic Musical Text. In: Younas, M., Awan, I., Petcu, D., Feng, B. (eds) Mobile Web and Intelligent Information Systems. MobiWIS 2024. Lecture Notes in Computer Science, vol 14792. Springer, Cham.
ISBN: 978-3-031-68004-5, DOI: https://doi.org/10.1007/978-3-031-68005-2_17

Della Ventura, M. (2024). Artificial Intelligence Literacy to Enhance Teacher Critical Thinking. In: Cheng, YP., Pedaste, M., Bardone, E., Huang, YM. (eds) Innovative Technologies and Learning. ICITL 2024. Lecture Notes in Computer Science, vol 14785. Springer, Cham.
ISBN: 978-3-031-65880-8, DOI: https://doi.org/10.1007/978-3-031-65881-5_19

Della Ventura, M. (2024). ICT: Inclusive Competences for Teaching. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the Third International Conference on Innovations in Computing Research (ICR’24). ICR 2024. Lecture Notes in Networks and Systems, vol 1058. Springer, Cham.
ISBN: 978-3-031-65521-0, DOI: https://doi.org/10.1007/978-3-031-65522-7_34

Della Ventura, M. (2024). A Deep Learning Algorithm for the Development of Meaningful Learning in the Harmonization of a Musical Melody. In: Lopata, A., Gudonienė, D., Butkienė, R. (eds) Information and Software Technologies. ICIST 2023. Communications in Computer and Information Science, vol 1979. Springer, Cham.
ISBN: 978-3-031-48980-8, DOI: https://doi.org/10.1007/978-3-031-48981-5_1

Della Ventura, M. (2023). Intelligent Tutoring System and Learning: Complexity and Resilience. In: Dascalu, M., Mealha, O., Virkus, S. (eds) Smart Learning Ecosystems as Engines of the Green and Digital Transition. SLERD 2023. Advances in Sustainability Science and Technology. Springer, Singapore.
ISBN: 978-981-99-5790-3, DOI: https://doi.org/10.1007/978-981-99-5540-4_3

Della Ventura, M. (2023). Intelligent (Musical) Tutoring System: The Strategic Sense for Deep Learning? In: Huang, YM., Rocha, T. (eds) Innovative Technologies and Learning. ICITL 2023. Lecture Notes in Computer Science, vol 14099. Springer, Cham.
ISBN: 978-3-031-40112-1, DOI: https://doi.org/10.1007/978-3-031-40113-8_1

Della Ventura, M. (2023). Human-Centred Artificial Intelligence in Sound Perception and Music Composition. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 646. Springer, Cham.
ISBN: 978-3-031-27439-8, DOI: https://doi.org/10.1007/978-3-031-27440-4_21

Della Ventura, M. (2022). A Self-learning Musical Tool to Support the Educational Activity. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 543. Springer, Cham.
ISBN: 978-3-031-16077-6, DOI: https://doi.org/10.1007/978-3-031-16078-3_3

Della Ventura, M. (2022). Compensatory Skill: The Dyslexia’s Key to Functionally Integrate Strategies and Technologies. In: Uden, L., Liberona, D. (eds) Learning Technology for Education Challenges. LTEC 2022. Communications in Computer and Information Science, vol 1595. Springer, Cham.
ISBN: 978-3-031-08889-6, DOI: https://doi.org/10.1007/978-3-031-08890-2_12

Della Ventura, M. (2022). A Self-adaptive Learning Music Composition Algorithm as Virtual Tutor. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 646. Springer, Cham.
ISBN: 978-3-031-08332-7, DOI: https://doi.org/10.1007/978-3-031-08333-4_2

Della Ventura, M. (2021). Automatic Recognition of Key Modulations in Symbolic Musical Pieces Using Information Theory. In: Arai K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 294. Springer, Cham.
ISBN: 978-3-030-82192-0, DOI: https://doi.org/10.1007/978-3-030-82193-7_56

Lariccia, S., Lariccia, G., Gabrieli, M., Della Ventura, M., Toffoli, G., Montanari, M.(2021). Museup: how virtual choirs may help students learning to learn. In: International Technology, Education and Development Conference INTED 2021 Proceedings.
ISBN: 978-84-09-27666-0, ISSN: 2340-1079, DOI: http://dx.doi.org/10.21125/inted.2021.1360

Della Ventura, M. (2021). Implementation of an Automatic Musical Scores Recognition System. In: Haber P., Lampoltshammer T., Mayr M., Plankensteiner K. (eds) Data Science – Analytics and Applications. Springer Vieweg, Wiesbaden.
ISBN: 978-3-658-32181-9, DOI: https://doi.org/10.1007/978-3-658-32182-6_8

Della Ventura, M. (2020). Removing Digital Natives from Technological Illiteracy with the Weblog. In: Huang TC., Wu TT., Barroso J., Sandnes F.E., Martins P., Huang YM. (eds) Innovative Technologies and Learning. ICITL 2020. Lecture Notes in Computer Science, vol 12555. Springer, Cham.
ISBN: 978-3-030-63884-9, DOI: https://doi.org/10.1007/978-3-030-63885-6_65

Della Ventura, M. (2020). Analytical Techniques for the Identification of a Musical Score: The Musical DNA. In: Krzhizhanovskaya V. et al. (eds) Computational Science – ICCS 2020. ICCS 2020. Lecture Notes in Computer Science, vol 12141. Springer, Cham.
ISBN: 978-3-030-50425-0, DOI: https://doi.org/10.1007/978-3-030-50426-7_3

Della Ventura, M. (2020). Symbolic Music Text Fingerprinting: Automatic Identification of Musical Scores. In: Czarnowski I., Howlett R., Jain L. (eds) Intelligent Decision Technologies. IDT 2020. Smart Innovation, Systems and Technologies, vol 193. Springer, Singapore.
ISBN: 978-981-15-5924-2, DOI: https://doi.org/10.1007/978-981-15-5925-9_22

Della Ventura, M. (2019). Speech Assessment Based on Entropy and Similarity Measures. In: Le Thi H., Le H., Pham Dinh T., Nguyen N. (eds) Advanced Computational Methods for Knowledge Engineering. ICCSAMA 2019. Advances in Intelligent Systems and Computing, vol 1121. Springer, Cham.
ISBN: 978-3-030-38363-3, DOI: https://doi.org/10.1007/978-3-030-38364-0_20

Della Ventura, M. (2019) Between Research and Action: The Generative Sense of Technology. In: Rønningsbakk L., Wu TT., Sandnes F., Huang YM. (eds) Innovative Technologies and Learning. ICITL 2019. Lecture Notes in Computer Science, vol 11937, pp. 754-763. Springer, Cham.
ISBN: 978-3-030-35342-1, DOI: https://doi.org/10.1007/978-3-030-35343-8_78

Della Ventura, M. (2019) Exploring the Impact of Artificial Intelligence in Music Education to Enhance the Dyslexic Student’s Skills. In: Uden L., Liberona D., Sanchez G., Rodríguez-González S. (eds) Learning Technology for Education Challenges. LTEC 2019. Communications in Computer and Information Science, vol 1011. Springer, Cham.
ISBN: 978-3-030-20797-7, DOI: https://doi.org/10.1007/978-3-030-20798-4_2

E.E. Mahmut, M. Della Ventura, D. Berian, V. Stoicutivadar (2019). Entropy-Based Dyslalia Screening, In proceeding of the 17th International Conference on Informatics, Management and Technology in Healthcare, 2019 Jul 4;262:252-255, Athens, Greece.
ISBN: 978-1-61499-986-7, DOI: 10.3233/SHTI190066

Della Ventura, M. (2019). Monitoring the Learning Process to Enhance Motivation by Means of Learning by Discovery Using Facebook. In: Ma W., Chan W., Cheng C. (eds) Shaping the Future of Education, Communication and Technology. Educational Communications and Technology Yearbook. Springer, Singapore.
ISBN: 978-981-13-6680-2, DOI: https://doi.org/10.1007/978-981-13-6681-9_9

Della Ventura, M. (2018). Shaping the Music Perception of an Automatic Music Composition: An Empirical Approach for Modelling Music Expressiveness. In Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018), Porto, Portugal, Springer vol 942.
ISBN: 978-3-030-17064-6, DOI: https://doi.org/10.1007/978-3-030-17065-3_1

Della Ventura, M. (2018). Computer System for Designing Musical Expressiveness in an Automatic Music Composition Process. In Proceedings of the International Conference on Bio-inspired Computing: Theories and Applications (BIC-TA 2018), Hong Kongi, People's Republic of China, Springer.
ISBN: 978-981-13-2825-1, DOI: https://doi.org/10.1007/978-981-13-2826-8_38

Della Ventura, M. (2018). Twitter As A Music Education Tool To Enhance The Learning Process: Conversation Analysis. In Proceedings of the International Conference on New Media for Educational Change: Effect on Learning and Reflection on Practice (HKAECT 2018), Hong Kong, People's Republic of China, Springer.
ISBN: 978-981-10-8895-7, DOI: https://doi.org/10.1007/978-981-10-8896-4_7

Della Ventura, M. (2018). Mobile Teaching: Remodel Music Technology Education to Engage Students. In Proceedings of the International Conference on Education and E-Learning (ICEEL 2018), Budapest, Hungary.
ISBN: 978-93-87954-62-5, DOI: http://dx.doionline.org/dx/ISERD.25052018.8065

Della Ventura, M. (2018). Voice Separation in Polyphonic Music: Information Theory Approach. In Proceedings of the 14th International Conference on Artificial Intelligence Applications and Innovations (AIAI 2018), Rhodes, Greece, Springer.
ISBN: 978-3-319-92006-1, DOI: https://doi.org/10.1007/978-3-319-92007-8_54

Della Ventura, M. (2018). Using Twitter to Enhance the Students Skills: Motivation a Disregarded Factor in Educational Design. In Proceedings of the 4th International Conference on e-Learning e-Education and Online Training (eLEOT 2018), Shanghai, People's Republic of China, Springer.
ISBN 978-3-319-93719-9, DOI: http://dx.doi.org/10.1007/978-3-319-93719-9_46

Della Ventura, M. (2017). Smart On-Line Technologies to Enhance Musical Kmowledge: A Qualitative Case Study. In Proceedings of the 6th International Conference on E-Learning and E-technology in Education (ICEEE 2017), Lodz, Poland.
ISBN: 978-1-941968-44-4

Della Ventura, M. (2017). Technology-Enhanced CLIL: Quality Indicators for the Analysis of an On-Line CLIL Course. In Proceedings of the 4th International Conference on Smart Innovation, Systems and Technologies (SEEL 2017), Vilamoura, Portugal, Springer.
ISBN: 978-3-319-59450-7, DOI: https://doi.org/10.1007/978-3-319-59451-4_34

Della Ventura, M. (2017). Similarity Measures for Music Information Retrieval. In Proceedings of the 5th International Conference on Computer Science, Apllied Mathematics and Applications (ICCSAMA 2017), Berlin, Germany, Springer.
ISBN: 978-3-319-61910-1, DOI: https://doi.org/10.1007/978-3-319-61911-8_15

Della Ventura, M. (2017). Peer Music Education For Social Sounds in a CLIL Classroom. In Proceedings of the 14th International Conference on Information Technology: New Generation (ITNG 2017), Las Vegas, USA, Springer.
ISBN: 978-3-319-54978-1, DOI: https://doi.org/10.1007/978-3-319-54978-1_47

Della Ventura, M. (2016). Automatic Music Composition from a Self-learning Algorithm. In: Ravulakollu K., Khan M., Abraham A. (eds) Trends in Ambient Intelligent Systems. Studies in Computational Intelligence, vol 633. Springer, Cham.
ISBN: 978-3-319-30182-2, DOI: https://doi.org/10.1007/978-3-319-30184-6_9

Della Ventura, M. (2016). A Learning Approach to Hierarchical Features for Automatic Music Composition. In Proceedings of the 3rd Euro-China Conference on Intelligent Data Analysis and Applications (ECC 2016), Fuzhou, China, Springer.
ISBN: 978-3-319-48499-0, DOI: https://doi.org/10.1007/978-3-319-48499-0_24

Della Ventura, M. (2016). Creating Inspiring Learning Environments by Means of Digital Technologies: A Case Study of the Effectiveness of WhatsApp in Music Education. In Proceedings of the 3rd International Conference on E-Learning, E-Education, and Online Training, Dublin, Ireland, Springer, pp. 36-45.
ISBN: 978-3-319-49624-5, DOI: https://doi.org/10.1007/978-3-319-49625-2_5

Della Ventura, M. (2016). A Bayesian Approach to Classify the Music Scores on the basis of the Music Style. In Proceedings of the 8th 8th International KES Conference on Intelligent Decision Technologies, Puerto de la Cruz, Tenerife, Spain, Springer.
ISBN: 978-3-319-39627-9, DOI: https://doi.org/10.1007/978-3-319-39627-9_15

Della Ventura, M. (2016). Using Mathematical Tools to Reduce the Combinatorial Explosion During the Automatic Segmentation of the Symbolic Musical Text. In Proceedings of the 4th International Conference on Computer Science, Applied Mathematics and Applications, Vienna, Austria, Springer.
ISBN: 978-3-319-38883-0, DOI: https://doi.org/10.1007/978-3-319-38884-7_20

Della Ventura, M. (2016). iPAD in Music Education: a Case Study of Collaborative Learning With Dyslexic Learners. In Proceedings of the International Conference on Interdisciplinary Social Science Studies , Oxford, UK, IOS Press.
ISBN 978-1-911185-02-4

Della Ventura, M. (2015). Automatic Tonal Music Composition Using Functional Harmony. Social Computing, Behavioral- Cultural Modeling and Prediction, Springer.
ISBN: 978-3-319-16267-6, DOI: https://doi.org/10.1007/978-3-319-16268-3_32

Fiocchetta, G., Della Ventura, M. (2015). Motivational Influences in a Transnational Music Virtual Studio: A Qualitative Case Study. Workshop Proceedings of the 11th International Conference on Intelligent Environments, Prague, Czech Republic.
ISBN 978-1-61499-529-6

Della Ventura, M. (2015). The Influence of the Rhythm with the Pitch on Melodic Segmentation. In Proceedings of the Second Euro-China Conference on Intelligent Data Analysis and Applications (ECC 2015), Ostrava, Czech Republic, Springer.
ISBN 978-3-419-21205-0, DOi: https://doi.org/10.1007/978-3-319-21206-7_17

Della Ventura, M. (2015). E-Learning Indicators to Improve the Effectiveness of the Learning Process. In Proceedings of the International Conference on E-Learning in The Workplace (ICELW 2015), New York, USA.
ISBN 978-0-9827670-5-4

Della Ventura, M. (2014). Process, Project and Problem Based Learning as a Strategy for Knowledge Building in Music Technology. In Proceedings of the Multidisciplinay Academic Conference on Education, Teaching and e-Learning (MAC-EteL 2014), Prague, Czech Republic.
ISBN: 978-80-905442-7-7

Della Ventura, M. (2014). Music Technology: The Social Network as a Learning Resource. In the Proceedings of the 2nd International Conference on Computer Supported Education (COSUE 2014), Cambridge, Massachusetts, USA.
ISBN: 978-960-474-363-6

Della Ventura, M. (2013). Blended Learning and Sustainability in Music Education: Motivation to Learn. In Proceedings of the 11th WSEAS International Conference on E-Activities (EACTIVITIES 2013), Nanjing, China.
ISBN: 978-960-474-356-8

Della Ventura, M. (2013). Detection of Historical Period in Symbolic Music Data: Revisited Version. In Proceedings of the 12th International Conference on Telecommunications and Informatics (TELE-INFO 2013), Baltimora, USA.
ISBN: 978-960-474-330-8

Della Ventura, M. (2013). Evaluation of musical similarity on the symbolic level of the musical text. In Proceedings of the 15th International Conference on Artificial Intelligence (ICAI 2013), Las Vegas, USA.
ISBN: 1-60132-246-1

Della Ventura, M. (2013). Toward an Analysis of Polyphonic Music in The Textual Symbolic Segmentation. In Proceedings of the 2nd International Conference on Computer, Digital Comunications and Computing (ICDCC 2013), Brasov, Romania.
ISBN: 978-1-61804-194-4

Della Ventura, M. (2013). The "Concealed" Motif: Analysis and Identification. In Proceedings of the 12th International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED 2013), Cambridge, UK.
ISBN: 978-1-61804-162-3

Della Ventura, M. (2012). Influence of the harmonic/functional analysis on the musical execution: representation and algorithm. In Proceedings of the 3rd International Conference on Applied Informatics and Computing Theory (AICT 2012), Barcellona, Spain.
ISBN: 978-1-61804-130-2

Della Ventura, M. (2012). Rhythm analysis of the "Sonorous Continuum" and conjoint evaluation of the musical entropy. In Proceedings of the 13th International Conference on Acoustics & Music: Theory & Applications (AMTA 2012), Iasi (Romania).
ISBN: 978-1-61804-096-1

Della Ventura, M. (2011). Analysis of algorithms’ implementation for melodical operators in symbolical textual segmentation and connected evaluation of musical entropy. In Proceedings of the 2nd International Conference on Environment, Economics, Energy, Devices, Systems, Comunications, Computer, Mathematics, Drobeta Turnu Severin, Romania.
ISBN: 978-1-61804-044-2