Anna-Maria Christodoulou

Doctoral Research Fellow - RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion (IMV)

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Email a.m.christodoulou@imv.uio.no

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Visiting address 澳门葡京手机版app下载sv. 3A Harald Schjelderups hus 0373 Oslo

Postal address Postboks 1133 Blindern 0318 Oslo

Other affiliations Faculty of Humanities (Student)

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Academic interests

My research revolves around using and developing intelligent systems that assist music analysis. My PhD is focused on the use and development of theories and tools for Multimodal MIR (Music Information Retrieval). At the same time, I am passionate about making music research accessible across different cultures, educational backgrounds, and abilities.

Background

2014-2016: Diploma in Music Theory - Apollonian Conservatory

2020-2021: Minor in IT (specialized in Artificial Intelligence) - American College of Greece

2017-2022: MA in Sound Acoustics and Music Technology - National and Kapodistrian University of Athens

Tags: Music Information Retrieval, artificial intelligence, Music Analysis, Multimodality

Christodoulou, Anna-Maria; Lartillot, Olivier & Jensenius, Alexander Refsum (2024). Multimodal Music Datasets? Challenges and Future Goals in Music Processing. International Journal of Multimedia Information Retrieval. ISSN 2192-6611. doi: https:/doi.org/10.1007/s13735-024-00344-6.
Guo, Jinyue; Christodoulou, Anna-Maria; Laczko, Balint & Glette, Kyrre (2024). LVNS-RAVE: Diversified audio generation with RAVE and Latent Vector Novelty Search. In Li, Xiaodong & Handl, Julia (Ed.), GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery (ACM). ISSN 979-8-4007-0495-6. p. 667�C670. doi: 10.1145/3638530.3654432.
Riaz, Maham & Christodoulou, Anna-Maria (2023). Using SuperCollider with OSC Commands for Spatial Audio Control in a Multi-Speaker Setup. In Andreopoulou, Areti & Boren, Braxton (Ed.), Proceedings of the Audio Engineering Society 155th Convention. Audio Engineering Society, Inc.. Show summary
With the ever-increasing prevalence of technology, its application in various music-related processes, such as music composition and performance, has become increasingly prominent. One fascinating area where technology finds utility is in music performance, offering opportunities for extensive sound exploration and manipulation. In this paper, we introduce an approach utilizing SuperCollider and Open Sound Control (OSC) commands in a multi-speaker setup, enabling spatial audio control for a truly interactive audio spatialization experience. We delve into the musicological dimensions of these distinct methods, examining their integration within a live performance setting to uncover their artistic and expressive potential. By merging technology and musicology, our research aims to unlock new avenues for immersive and captivating musical experiences.

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Christodoulou, Anna-Maria & Jensenius, Alexander Refsum (2024). Navigating Challenges in Multimodal Music Data Management for AI Systems.
Christodoulou, Anna-Maria; Dutta, Sagar; Lartillot, Olivier; Glette, Kyrre & Jensenius, Alexander Refsum (2024). Exploring Convolutional Neural Network Models for Multimodal Classification of Expressive Piano Performance.
Christodoulou, Anna-Maria; Lartillot, Olivier & Anagnostopoulou, Christina (2023). Greek Folk Music Dataset.
Christodoulou, Anna-Maria; Lartillot, Olivier & Anagnostopoulou, Christina (2023). Computational Analysis of Greek Folk Music of the Aegean.
Christodoulou, Anna-Maria (2023). Workshop on Introduction to Multimodal Music Analysis.

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Published Jan. 31, 2023 4:34 PM - Last modified Oct. 12, 2023 2:42 PM