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Biography
Dr. Anna Shvets is an assistant professor in Interactive Music Technology at the Mixed Reality Lab of the University of Nottingham. With a PhD in computational musicology, her career reflects a deep commitment to exploring the intersection of creative expression and computational innovation. Her work seamlessly integrates classical music training with advanced technological expertise, particularly in the fields of virtual reality (VR) music applications and generative artificial intelligence (AI) models.
An award-winning composer and pianist, as well as a certified deep learning engineer and full-stack web developer, Dr. Shvets has been recognized for her contributions to programming and VR projects. She has also received research grants from the Polish Ministry of Science and Higher Education. As a professional composer affiliated with SACEM and an active member of associations advocating for female film composers in France and the United States, her dedication to the creative process transcends traditional boundaries. She holds a French National Council of Universities (CNU) qualification as a lecturer and has built a diverse educational portfolio that supports her interdisciplinary research.
In her research on generative AI, Dr. Shvets has made significant academic contributions, including seven publications advancing the field of generative AI. She pioneered the development of conditional GAN and spiking convLSTM models, positioning her work at the forefront of AI research. Her insights on "Conditional Music Generation," presented at EVA London in 2023, highlight her deep understanding of both theoretical and practical aspects of generative AI. Her contributions have been officially recognized by the Association for Advancement in Artificial Intelligence (AAAI) in Washington in 2023.
Dr. Shvets is also a pioneer in volumetric music composition, introducing this innovative genre into Music XR. Her leadership in launching artistic and educational initiatives in VR, coupled with her role within AFXR, highlights her visionary approach. Her talk on "Generative AI in a VR Context" at VRJAM Toulouse 2023 and "Genrative AI for Audio-Visual Domain" at the professional event organized by Commission Supérieure Technique de l'Image et du Son in Paris reaffirmed her expertise in cutting-edge research in Music XR.
Her extensive experience as a reviewer for over a decade for major digital art events, including those organized by the British Computer Arts Society, has enriched her understanding of contemporary digital art aesthetics and practices.
Expertise Summary
Dr. Anna Shvets is an accomplished interdisciplinary researcher, composer, and educator, specializing in the convergence of music, technology, and digital humanities. With a PhD in Computational Musicology and a Lecturer qualification from the French National Council of Universities, her academic work integrates classical music training with cutting-edge technologies, such as AI and virtual reality (VR). Dr. Shvets' contributions extend across computational music analysis, AI-driven music generation, and innovative approaches to music education.
Her research has pioneered the development of generative spiking neural networks, with a focus on sustainable AI for creating interactive audio-visual experiences in VR. These projects merge technical innovation with artistic expression, redefining immersive musical experiences. Notably, her VR project Omega introduced volumetric music composition, a new genre within music extended reality (XR), earning her the "Coup de Cœur" prize at VRJAM Toulouse 2023.
Dr. Shvets has made significant strides in music education through her development of graph-based systems for teaching harmony. These innovations include mobile applications such as award winning Jeu d'harmonie and Harmonic Constructor, recognized for their educational impact. Her graph system offers a new visual framework for harmonic analysis, applied in both educational and analytical contexts, and has been integrated into interactive web and mobile applications that enhance student learning.
Her participation in prominent scientific seminars and conferences, combined with programming and AI scholarships from Intel, IBM, and Google, underscores her commitment to technological advancement. Dr. Shvets' research has been published extensively in international conferences, where she has explored topics ranging from the mathematical analysis of music form in the works of Arvo Pärt to the integration of spiking neural networks for music generation. Her interdisciplinary approach bridges music theory, cognitive science, and AI, making her a leader in the fields of music technology and education. Through her innovative projects, such as Interlace, Atmos, and TA VR Player, she continues to push the boundaries of what is possible in the intersection of music and digital technology.
Teaching Summary
Dr. Anna Shvets' teaching and learning interests revolve around the innovative integration of technology, AI, and pedagogical methodologies in music education. She has been at the forefront of using… read more
Research Summary
Dr. Anna Shvets' research focuses on the development of sustainable generative AI (genAI) models for creating interactive, immersive audio-visual experiences in virtual reality (VR). Her work… read more
Selected Publications
2024: Interlace (VR project in Unreal Engine, collaboration with Anthony Trzepizur)
2024: TA VR Player (VR project in Unreal Engine)
2023: Omega (VR project, collaboration with Samer Darkazanli)
2023: Atmos (VR project, collaboration with Samer Darkazanli)
2020: Graphs in music harmony (VR project, collaboration with Samer Darkazanli)
2016: Jeu d'harmonie (Android OS)
2015: Harmonic Constructor (Android OS)
2014: Schemographe (Android OS)
2013: Interactive web applications for learning harmony (Web)
2013: Mein Weg application for interactive form visualization of the organ piece by Arvo Pärt (Desktop - Windows, Mac, Linux OS)
The Interlace, Omega, Atmos and TA VR Player VR projects merge technical innovation with artistic expression, demonstrating the potential of VR technology in redefining musical experiences. These projects, alongside Graphs in Music Harmony VR application, Jeu d'harmonie, and Harmonic Constructor mobile applications, serve as educational tools as well as bridges between complex music theory and practical learning, enhancing music education through interactive digital formats.
Furthermore, the Schemographe and Mein Weg applications are distinguished by their use of original analytical methods, presented in an interactive and engaging manner that captivates users while advancing theoretical understanding.
Dr. Anna Shvets' teaching and learning interests revolve around the innovative integration of technology, AI, and pedagogical methodologies in music education. She has been at the forefront of using graph-based systems for representing and teaching tonal harmony, particularly through the development of interactive mobile and web applications. Her research has demonstrated that blended learning approaches, combining traditional instruction with interactive digital tools, significantly improve student performance.
Her experience extends into mentoring, having participated for two years as a mentor in the Mentor'IA program, an initiative by ANITI in Toulouse, where she guided students and early-stage researchers in the application of AI to various fields. This mentorship experience aligns with her commitment to fostering interdisciplinary skills and technological fluency in her students.
Furthermore, Dr. Shvets conducted several tutorials, one of them dedicated to the development of spiking neural networks at the "EVA London 2022" international conference. This tutorial introduced participants to advanced AI techniques, focusing on sustainable spiking neural networks and their application in music generation and other creative fields, reinforcing her teaching philosophy of blending cutting-edge AI research with practical applications in the arts.
Current Research
Dr. Anna Shvets' research focuses on the development of sustainable generative AI (genAI) models for creating interactive, immersive audio-visual experiences in virtual reality (VR). Her work integrates advanced techniques in neuromorphic computing and AI to design efficient models that power real-time, dynamic music and visual experiences, with a particular emphasis on ecological sustainability. Through projects such as Spiking ConvLSTM for Semantic Music Generation and Volumetric Music Composition in a VR Context, Dr. Shvets explores the intersection of music and technology, pushing the boundaries of generative AI in both artistic and educational domains.
Recent research explores the possibilities of Metasound within Unreal Engine 5 to create interactive dynamic music experiences. This work focuses on building fully responsive, real-time music environments that adapt to user interaction, offering new opportunities for immersive and dynamic music composition.
Additionally, Dr. Shvets is pioneering the development of metaverse experiences aimed at immersive music learning, with projects like Graphs in Harmony Learning and Adaptive VR Test in Music Harmony leveraging generative AI and VR to create innovative educational tools. Her research explores non-linear and adaptive music composition methods that can be used in interactive VR settings for music education.
Dr. Shvets has made significant contributions to the field, including a series of publications that showcase the transformation of traditional music methods into interactive VR experiences. These include Combining a Time-Distributed Data Generator with the Niagara Particle System and Volumetric Music Composition in a VR Context, where she has applied neuromorphic computing methods to 2D audio-visual artworks, transforming them into immersive 3D experiences. Her research also addresses the challenges of creating AI-driven music models that can generate diatonic harmonic sequences and semantic music content in real time, which she has detailed in various works presented at EVA and TENOR conferences.
By combining cutting-edge AI technologies with creative music processes, Dr. Shvets' work continues to bridge the gap between technology and the arts, contributing innovative solutions for both the artistic and educational sectors in VR and AI-based music composition.
Past Research
Dr. Anna Shvets' previous research spans a wide range of interdisciplinary approaches, focusing on the integration of technology and cognitive science into music education, analysis, and composition. Her work has explored innovative methods in tonal harmony, graph-based representations, and the mathematical foundations of musical form, while also advancing the development of e-learning tools and applications for music education.
Graph-Based Representation in Music Harmony: Dr. Shvets developed a graph-based system for representing harmonic structures, which simplifies the understanding and analysis of tonal harmony. This system is structured as a network of harmonic relationships, visualized through graphs, and has been applied to both educational and analytical contexts. A significant portion of Dr. Shvets' research has focused on the application of graph systems for music analysis and education. These studies formed the basis for the interactive e-learning applications she later developed, contributing to significant advancements in distance learning for music theory.
Mathematical and Cognitive Foundations in Music Analysis: Dr. Shvets has extensively studied the mathematical bases of musical form, particularly in the works of composer Arvo Pärt. Her 2014 publication, "Mathematical Bases of the Form Construction in Arvo Pärt's Music", explores the underlying mathematical logic of Pärt's compositions, revealing how pseudo-code can represent the form and structure of his works. This research, combined with her earlier work "'Interactive application for visualization of the form of written postmodern music" (2013), delves into the intersection of postmodernism and mathematical structures in music, providing deep insights into the construction of Pärt's unique compositional style.
Music Modeling: Her research on Arvo Pärt's music was crucial in modeling musical form in the OpenMusic environment, as described in her work "Modelling Arvo Pärt's Music with OpenMusic" (2014). This research demonstrated how Pärt's musical form could be understood through mathematical logic and applied in algorithmic music composition. Her approach to analyzing and modeling music using AI and mathematical principles has contributed to both theoretical and practical advancements in the field.
Impact on Music Education: Throughout her research, Dr. Shvets has remained focused on improving music education through technology. Her work on visualization strategies in music harmony courses ("Visualization Strategies in the E-learning Course on Music Harmony", 2016) demonstrated that students benefited from graph-based representations of harmonic relationships, resulting in better retention and comprehension. Similarly, in her book chapter "Visualization Impact on the Effectiveness of Music Harmony Knowledge Assimilation" (2015), she emphasized the role of visual tools in enhancing the learning experience.
Conclusion: Dr. Shvets' research has consistently aimed at bridging the gap between traditional music theory and modern technological advancements. From graph-based representations of harmony to cognitive approaches in e-learning, her contributions have revolutionized music education and analysis, offering new frameworks for teaching, understanding, and composing music. Her interdisciplinary work continues to drive innovation in music technology, AI, and education, making her a leader in the field of musicology and technology integration.
Future Research
Dr. Anna Shvets' future research will focus on the development of brain-computer interfaces (BCIs) for enhancing human-computer interaction (HCI) patterns, utilizing spiking neural networks (SNNs) and immersive virtual reality (VR) environments. By integrating BCIs with SNNs, this research aims to enable real-time, efficient, and energy-conscious interactions between the human brain and digital systems, making strides toward more intuitive and adaptive user experiences.
At the core of this work is the exploration of SNNs, known for their low power consumption and biological inspiration, which will drive more efficient processing in the BCI systems. These networks will enable the direct mapping of neural activity to complex tasks within VR environments, enhancing user engagement by leveraging real-time feedback loops between brain signals and immersive content.
In collaboration with her ongoing research in generative AI and VR, Dr. Shvets plans to explore how these advanced neural models can not only facilitate more natural HCI patterns but also contribute to interactive and dynamic music and visual experiences. This research will potentially revolutionize fields such as interactive media, cognitive rehabilitation, and immersive education by creating more responsive and adaptive virtual environments tailored to individual users' brain activity.
By combining BCIs with her existing expertise in VR and AI-driven music technologies, Dr. Shvets aims to develop applications where users can control immersive environments through brain signals. These applications will enhance the accessibility of VR technologies, open new possibilities for creative expression, and create novel educational tools that adapt to individual cognitive states.
This future research will also address critical challenges such as reducing latency in BCI systems, improving accuracy in interpreting neural data, and ensuring ethical considerations regarding data privacy and bias in AI-driven models. By building on her experience with sustainable AI models, Dr. Shvets' research will further explore ecologically responsible approaches to the development of cutting-edge BCIs, ensuring they are energy-efficient and scalable for real-world applications.
SHVETS, ANNA, 2024. Swan Distrokid. SHVETS, ANNA, 2024. Omega Distrokid. KAUSTUBH D. DHOLE, VARUN GANGAL, SEBASTIAN GEHRMANN, AADESH GUPTA, ZHENHAO LI, SAAD MAHAMOOD, ABINAYA MAHENDIRAN, SIMON MILLE, ASHISH SRIVASTAVA, SAMSON TAN, TONGSHUANG WU, JASCHA SOHL-DICKSTEIN, JINHO D. CHOI, EDUARD H. HOVY, ONDREJ DUSEK, SEBASTIAN RUDER, SAJANT ANAND, NAGENDER ANEJA, RABIN BANJADE, LISA BARTHE, HANNA BEHNKE, IAN BERLOT-ATTWELL, CONNOR BOYLE, CAROLINE BRUN, MARCO ANTONIO SOBREVILLA CABEZUDO, SAMUEL CAHYAWIJAYA, EMILE CHAPUIS, WANXIANG CHE, MUKUND CHOUDHARY, CHRISTIAN CLAUSS, PIERRE COLOMBO, FILIP CORNELL, GAUTIER DAGAN, MAYUKH DAS, TANAY DIXIT, THOMAS DOPIERRE, PAUL-ALEXIS DRAY, SUCHITRA DUBEY, TATIANA EKEINHOR, MARCO DI GIOVANNI, TANYA GOYAL, RISHABH GUPTA, LOUANES HAMLA, SANG HAN, FABRICE HAREL-CANADA, ANTOINE HONORE, ISHAN JINDAL, PRZEMYSLAW K. JONIAK, DENIS KLEYKO, VENELIN KOVATCHEV, KALPESH KRISHNA, ASHUTOSH KUMAR, STEFAN LANGER, SEUNGJAE RYAN LEE, COREY JAMES LEVINSON, HUALOU LIANG, KAIZHAO LIANG, ZHEXIONG LIU, ANDREY LUKYANENKO, VUKOSI MARIVATE, GERARD DE MELO, SIMON MEONI, MAXIME MEYER, AFNAN MIR, NAFISE SADAT MOOSAVI, NIKLAS MUENNIGHOFF, TIMOTHY SUM HON MUN, KENTON MURRAY, MARCIN NAMYSL, MARIA OBEDKOVA, PRITI OLI, NIVRANSHU PASRICHA, JAN PFISTER, RICHARD PLANT, VINAY PRABHU, VASILE PAIS, LIBO QIN, SHAHAB RAJI, PAWAN KUMAR RAJPOOT, VIKAS RAUNAK, ROY RINBERG, NICHOLAS ROBERTS, JUAN DIEGO RODRIGUEZ, CLAUDE ROUX, PAULO HENRIQUE SANTOS VASCONCELLOS, ANANYA B. SAI, ROBIN M. SCHMIDT, THOMAS SCIALOM, TSHEPHISHO SEFARA, SAQIB SHAMSI, XUDONG SHEN, YIWEN SHI, HAOYUE SHI, ANNA SHVETS, NICK SIEGEL, DAMIEN SILEO, JAMIE SIMON, CHANDAN SINGH, ROMAN SITELEW, PRIYANK SONI, TAYLOR SORENSEN, WILLIAM SOTO, AMAN SRIVASTAVA, K. V. ADITYA SRIVATSA, TONY SUN, MUKUND VARMA T., A. TABASSUM, FIONA ANTING TAN, RYAN TEEHAN, MO TIWARI, MARIE TOLKIEHN, ATHENA WANG, ZIJIAN WANG, ZIJIE J. WANG, GLORIA WANG, FUXUAN WEI, BRYAN WILIE, GENTA INDRA WINATA, XINYI WU, WITOLD WYDMANSKI, TIANBAO XIE, USAMA YASEEN, MICHAEL A. YEE, JING ZHANG and YUE ZHANG, 2023. NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation Northern European Journal of Language Technology. 9(1), 1-41 GEHRMANN, SEBASTIAN, BHATTACHARJEE, ABHIK, MAHENDIRAN, ABINAYA, WANG, ALEX, PAPANGELIS, ALEXANDROS, MADAAN, AMAN, MCMILLAN-MAJOR, ANGELINA, SHVETS, ANNA, UPADHYAY, ASHISH, BOHNET, BERND, YAO, BINGSHENG, WILIE, BRYAN, BHAGAVATULA, CHANDRA, YOU, CHAOBIN, THOMSON, CRAIG, GARBACEA, CRISTINA, WANG, DAKUO, DEUTSCH, DANIEL, XIONG, DEYI, JIN, DI, GKATZIA, DIMITRA, RADEV, DRAGOMIR, CLARK, ELIZABETH, DURMUS, ESIN, LADHAK, FAISAL, GINTER, FILIP, WINATA, GENTA INDRA, STROBELT, HENDRIK, HAYASHI, HIROAKI, NOVIKOVA, JEKATERINA, KANERVA, JENNA, CHIM, JENNY, ZHOU, JIAWEI, CLIVE, JORDAN, MAYNEZ, JOSHUA, SEDOC, JOÃO, JURASKA, JURAJ, DHOLE, KAUSTUBH, CHANDU, KHYATHI RAGHAVI, BELTRACHINI, LAURA PEREZ, RIBEIRO, LEONARDO F . R., TUNSTALL, LEWIS, ZHANG, LI, PUSHKARNA, MAHIM, CREUTZ, MATHIAS, WHITE, MICHAEL, KALE, MIHIR SANJAY, EDDINE, MOUSSA KAMAL, DAHEIM, NICO, SUBRAMANI, NISHANT, DUSEK, ONDREJ, LIANG, PAUL PU, AMMANAMANCHI, PAWAN SASANKA, ZHU, QI, PUDUPPULLY, RATISH, KRIZ, RENO, SHAHRIYAR, RIFAT, CARDENAS, RONALD, MAHAMOOD, SAAD, OSEI, SALOMEY, CAHYAWIJAYA, SAMUEL, VSTAJNER, SANJA, MONTELLA, SEBASTIEN, JOLLY, SHAILZA, MILLE, SIMON, HASAN, TAHMID, SHEN, TIANHAO, ADEWUMI, TOSIN, RAUNAK, VIKAS, RAHEJA, VIPUL, NIKOLAEV, VITALY, TSAI, VIVIAN, JERNITE, YACINE, XU, YING, SANG, YISI, LIU, YIXIN and HOU, YUFANG, 2022. GEMv2: Multilingual NLG Benchmarking in a Single Line of Code In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 266-281 SHVETS, ANNA, 2021. System Description for the CommonGen task with the POINTER model. In: BOSSELNUT, ANTOINE, DURMUS, ESIN, GANGAL, VARUN PRASHANT, GEHRMANN, SEBASTIAN, JERNITE, YACINE, PEREZ-BELTRACHINI, LAURA, SHAIKH, SAMIRA and XU, WEI, eds., Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021) Association for Computational Linguistics. 161–165 SHVETS, ANNA, 2020. The use of flowcharts for representation of a creative process by Arvo Pärt At: Archives of Arvo Pärt Centre
SHVETS, ANNA and SHVETS, VALENTYNA, 2018. Cognitive and technological aspects of e-learning in context of robotization Cognitive Science – New Media – Education. 65-78 SHVETS, ANNA and JEMIELNIK, JOANNA, 2016. Visualization Strategies in the E-learning Course on Music Harmony at University – Effects and Possibilities of Use. In: ANDRZEJ MICHALSKI, ed., Music Pedagogy. Ideals. Values. Pragmatics Athenae Gedanenses. 218-229
SHVETS, ANNA and DESAINTE-CATHERINE, MYRIAM, 2015. Schemographe: Application for a New Representation Technique and Methodology of Analysis in Tonal Harmony Lecture Notes in Computer Science: Evolutionary and Biologically Inspired Music, Sound, Art and Design. 9027, 212-223 JEMIELNIK, JOANNA and SHVETS, ANNA, 2015. Visualization impact on the effectiveness of music harmony knowledge assimilation. In: BOGUSZEWSKA, ANNA and NIŚCIOR, BARBARA, eds., Art and education. Music art Department of Arts of Maria Curie- Skłodowska University. 47-61
SHVETS, ANNA, 2014. Mathematical bases of the form construction in Arvo Pärt’s music Lietuvos muzikologija (Lithuanian Musicology). 15, 88-101 SHVETS, ANNA and DE PAIVA SANTANA, CHARLES, 2014. Modelling Arvo Pärt’s music with OpenMusic In: Proceedings of the 2014 International Conference on Electronic Visualisation and the Arts (EVA London 2014). 9-16 SHVETS, ANNA, 2012. Mein Weg” by Arvo Pärt in the context of postmodernism Scientific almanac of the Lviv University: Art Studies. 31-44
SHVETS, GANNA, 2009. Principles of platform MOODLE application in remote testing in “Music harmony” course In: Proceedings of the VI scientific conference on modern tendencies of info-communication technologies development in education. 159
SHVETS, GANNA, 2008. Application of graphs as a visual image of frames in distance learning of harmony fundamentals Scientific almanac of Ukrainian National Musical Academy n. P.I. Tchaikovsky. 79, 119-125
SHVETS, VALENYNA and SHVETS, GANNA, 2006. Theoretical bases of programming of the learning activities of students during “diatonic” section studying of the music harmony course Scientific almanac “Theory and methods of art education”. 3(8), 83-88