Anna-Maria Christodoulou

• 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. 13(3). doi: 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.
• Wosch, Thomas; Vobig, Bastian; Lartillot, Olivier & Christodoulou, Anna-Maria (2023). HIGH-M (Human Interaction assessment and Generative segmentation in Health and Music).
• 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.
• Lartillot, Olivier; God?y, Rolf Inge & Christodoulou, Anna-Maria (2022). Computational detection and characterisation of sonic shapes: Towards a Toolbox des objets sonores. Show summary

  Computational detection and analysis of sound objects is of high importance both for musicology and sound design. Yet Music Information Retrieval technologies have so far been mostly focusing on transcription of music into notes in a classical sense whereas we are interested in detecting sound objects and their feature categories, as was suggested by Pierre Schaeffer¡¯s typology and morphology of sound objects in 1966, reflecting basic sound-producing action types. We propose a signal-processing based approach for segmentation, based on a tracking of the salient characteristics over time, and dually Gestalt-based segmentation decisions based on changes. Tracking of pitched sound relies on partial tracking, whereas the analysis of noisy sound requires tracking of larger frequency bands possibly varying over time. The resulting sound objects are then described based on Schaeffer¡¯s taxonomy and morphology, expressed first in the form of numerical descriptors, each related to one type of taxonomy (percussive/sustained/iterative, stable/moving pitch vs unclear pitch) or morphology (such as grain). This multidimensional feature representation is further divided into discrete categories related to the different classes of sounds. The typological and morphological categorisation is driven by the theoretical and experimental framework of the morphodynamical theory. We first experiment on isolated sounds from the Solf¨¨ge des objets sonores¡ªwhich features a large variety of sound sources¡ªbefore considering more complex configurations featuring a succession of sound objects without silence or with simultaneous sound objects. Analytical results are visualised in the form of graphical representations, aimed both for musicology and music pedagogy purposes. This will be applied to the graphical descriptions of and browsing within large music catalogues. The application of the analytical descriptions to music creation is also investigated.

• Christodoulou, Anna-Maria; Anagnostopoulou, Christina & Lartillot, Olivier (2022). Computational Analysis of Greek folk music of the Aegean islands. National and Kapodistrian University of Athens.

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