Anna-Maria Christodoulou

• Christodoulou, Anna-Maria & Lartillot, Olivier (2025). A Multimodal Dataset of Greek Folk Music. I Luca, Elsa De (Red.), DLfM '25: Proceedings of the 12th International Conference on Digital Libraries for Musicology. Association for Computing Machinery (ACM). ISSN 9798400720833. s. 19¨C27. doi: https:/dl.acm.org/doi/10.1145/3748336.3748339. Vis sammendrag

  This paper presents a multimodal dataset of Greek folk dance music, focusing on syrtos and balos. Developed to support research in computational musicology, the dataset improves access to Greek musical heritage through manually transcribed MIDI scores, aligned lyrics, and rich metadata, all curated by expert musicologists. Through pattern analysis and feature extraction, we examine both shared melodic structures and unique characteristics of each dance, with some examples reflecting traces of oral transmission. While metadata accompanies the collection to support organization and context, our primary emphasis is on the musical and lyrical content. This work contributes to digital ethnomusicology by showing how multimodal datasets of folk music can inform both analytical research and cultural heritage preservation.

• Christodoulou, Anna-Maria; Glette, Kyrre; Lartillot, Olivier & Jensenius, Alexander Refsum (2025). MusiQAl: A Dataset for Music Question¨CAnswering through Audio¨CVideo Fusion. Transactions of the International Society for Music Information Retrieval. 8(1), s. 265¨C282. doi: 10.5334/tismir.222. Vis sammendrag

  Music question¨Canswering (MQA) is a machine learning task where a computational system analyzes and answers questions about music?related data. Traditional methods prioritize audio, overlooking visual and embodied aspects crucial to music performance understanding. We introduce MusiQAl, a multimodal dataset of 310 music performance videos and 11,793 human?annotated question¨Canswer pairs, spanning diverse musical traditions and styles. Grounded in musicology and music psychology, MusiQAl emphasizes multimodal reasoning, causal inference, and cross?cultural understanding of performer¨Cmusic interaction. We benchmark AVST and LAVISH architectures on MusiQAI, revealing strengths and limitations, underscoring the importance of integrating multimodal learning and domain expertise to advance MQA and music information retrieval.

• Guo, Jinyue; Christodoulou, Anna-Maria; Laczko, Balint & Glette, Kyrre (2024). LVNS-RAVE: Diversified audio generation with RAVE and Latent Vector Novelty Search. I Li, Xiaodong & Handl, Julia (Red.), GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery (ACM). ISSN 9798400704956. s. 667¨C670. doi: 10.1145/3638530.3654432. Vis sammendrag

  Evolutionary Algorithms and Generative Deep Learning have been two of the most powerful tools for sound generation tasks. However, they have limitations: Evolutionary Algorithms require complicated designs, posing challenges in control and achieving realistic sound generation. Generative Deep Learning models often copy from the dataset and lack creativity. In this paper, we propose LVNS-RAVE, a method to combine Evolutionary Algorithms and Generative Deep Learning to produce realistic and novel sounds. We use the RAVE model as the sound generator and the VGGish model as a novelty evaluator in the Latent Vector Novelty Search (LVNS) algorithm. The reported experiments show that the method can successfully generate diversified, novel audio samples under different mutation setups using different pre-trained RAVE models. The characteristics of the generation process can be easily controlled with the mutation parameters. The proposed algorithm can be a creative tool for sound artists and musicians.

• Christodoulou, Anna-Maria; Dutta, Sagar; Lartillot, Olivier Serge Gabriel; Glette, Kyrre & Jensenius, Alexander Refsum (2024). Exploring Convolutional Neural Network Models for Multimodal Classification of Expressive Piano Performance, Proceedings of the Sound and Music Computing Conference 2024. SMC Network. ISSN 9789893520758. Fulltekst i vitenarkiv Vis sammendrag

  This paper addresses improving performance analysis by automating the recognition of expressive performance styles. We propose a multimodal fusion approach integrating audio, video, and motion data. We demonstrate the effectiveness of our approach by utilizing convolutional neural network (CNN) models. Training is done on a classical piano dataset of 211 excerpts containing audio, video, MIDI, and motion capture data. The results highlight the robustness of the CNN models; they achieve high accuracy even when trained on a limited dataset. Our study contributes to advancing the field of performance analysis by applying deep learning techniques to multimodal data.

• 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. Fulltekst i vitenarkiv Vis sammendrag

  The term ¡°multimodal music dataset¡± is often used to describe music-related datasets that represent music as a multimedia art form and multimodal experience. However, the term ¡°multimodality¡± is often used differently in disciplines such as musicology, music psychology, and music technology. This paper proposes a definition of multimodality that works across different music disciplines. Many challenges are related to constructing, evaluating, and using multimodal music datasets. We provide a task-based categorization of multimodal datasets and suggest guidelines for their development. Diverse data pre-processing methods are illuminated, highlighting their contributions to transparent and reproducible music analysis. Additionally, evaluation metrics, methods, and benchmarks tailored for multimodal music processing tasks are scrutinized, empowering researchers to make informed decisions and facilitating cross-study comparisons.

• Riaz, Maham & Christodoulou, Anna-Maria (2023). Using SuperCollider with OSC Commands for Spatial Audio Control in a Multi-Speaker Setup. I Andreopoulou, Areti & Boren, Braxton (Red.), Proceedings of the Audio Engineering Society 155th Convention. Audio Engineering Society, Inc.. ISSN 9781713894667. Vis sammendrag

  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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• Arnim, Hugh Alexander von; Fleckenstein, Abbigail Marie & Christodoulou, Anna-Maria (2025). SysMus25 Conference Proceedings. Zenodo. 164 s. doi: 10.5281/zenodo.17632991.
• Fleckenstein, Abbigail Marie; Arnim, Hugh Alexander von & Christodoulou, Anna-Maria (2025). SysMus25 Book of Abstracts. Zenodo. 165 s. doi: 10.5281/zenodo.17531650.

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• Arnim, Hugh Alexander von; Christodoulou, Anna-Maria; Burnim, Kayla; Upham, Finn; Kelkar, Tejaswinee & Jensenius, Alexander Refsum (2025). LightHearted¡ªA Framework for Mapping ECG Signals to Light Parameters in Performing Arts.
• Christodoulou, Anna-Maria; Glette, Kyrre; Lartillot, Olivier & Jensenius, Alexander Refsum (2025). MusiQAl: Music Question Answering through Audio-Video fusion. doi: https:/ismir2025.ismir.net/program-detailed-schedule.
• Christodoulou, Anna-Maria (2025). The Benefits of Multimodal MIR in Computational Analysis of Music Therapy Improvisation. doi: https:/ifas.thws.de/fileadmin/user_upload/250917_HIGH-M_Symposium_Programme_updated.pdf.
• Christodoulou, Anna-Maria & Lartillot, Olivier (2025). A Multimodal Dataset of Greek Folk Music. doi: https:/dlfm.web.ox.ac.uk/2025-programme.
• 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 & Jensenius, Alexander Refsum (2024). Navigating Challenges in Multimodal Music Data Management for AI Systems. Vis sammendrag

  The responsible management of multimodal music datasets plays a crucial role in the development and evaluation of music processing systems. However, navigating the landscape of legal and ethical considerations can be a complex and challenging task due to the magnitude and diversity of such. This paper clarifies these divergent legal and ethical considerations and highlights the challenges associated with multimodality and AI systems. Focusing on the most crucial stages of multimodal music data management, we provide recommendations for tackling legal and ethical challenges. We emphasize the importance of establishing an inclusive and accessible music data environment, encouraging researchers and data users to adopt responsible approaches towards managing multimodal music data collections.

• Christodoulou, Anna-Maria (2023). Workshop on Introduction to Multimodal Music Analysis.
• Christodoulou, Anna-Maria; Lartillot, Olivier & Anagnostopoulou, Christina (2023). Computational Analysis of Greek Folk Music of the Aegean.
• Wosch, Thomas; Vobig, Bastian; Lartillot, Olivier & Christodoulou, Anna-Maria (2023). HIGH-M (Human Interaction assessment and Generative segmentation in Health and Music).
• Lartillot, Olivier; God?y, Rolf Inge & Christodoulou, Anna-Maria (2022). Computational detection and characterisation of sonic shapes: Towards a Toolbox des objets sonores. Vis sammendrag

  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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