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20242026
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cs.SD2026

How Far Can Pretrained LLMs Go in Symbolic Music? Controlled Comparisons of Supervised and Preference-based Adaptation

Deepak Kumar, Emmanouil Karystinaios, Gerhard Widmer +1

Music often shares notable parallels with language, motivating the use of pretrained large language models (LLMs) for symbolic music understanding and generation. Despite growing i…

cs.SD2025

MUSE-Explainer: Counterfactual Explanations for Symbolic Music Graph Classification Models

Baptiste Hilaire, Emmanouil Karystinaios, Gerhard Widmer

Interpretability is essential for deploying deep learning models in symbolic music analysis, yet most research emphasizes model performance over explanation. To address this, we in…

cs.SD2025

WeaveMuse: An Open Agentic System for Multimodal Music Understanding and Generation

Emmanouil Karystinaios

Agentic AI has been standardized in industry as a practical paradigm for coordinating specialized models and tools to solve complex multimodal tasks. In this work, we present Weave…

cs.SD2025

AnalysisGNN: Unified Music Analysis with Graph Neural Networks

Emmanouil Karystinaios, Johannes Hentschel, Markus Neuwirth +1

Recent years have seen a boom in computational approaches to music analysis, yet each one is typically tailored to a specific analytical domain. In this work, we introduce Analysis…

cs.SD2024

Language Models for Music Medicine Generation

Emmanouil Nikolakakis, Joann Ching, Emmanouil Karystinaios +3

Music therapy has been shown in recent years to provide multiple health benefits related to emotional wellness. In turn, maintaining a healthy emotional state has proven to be effe…