1 citations · 1 across the 9 of their papers we have counts for
9 papers
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…
Sound and Music Biases in Deep Music Transcription Models: A Systematic Analysis
Lukáš Samuel Marták, Patricia Hu, Gerhard Widmer
Automatic Music Transcription (AMT) -- the task of converting music audio into note representations -- has seen rapid progress, driven largely by deep learning systems. Due to the…
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…
EngravingGNN: A Hybrid Graph Neural Network for End-to-End Piano Score Engraving
Emmanouil Karystinaios, Francesco Foscarin, Gerhard Widmer
This paper focuses on automatic music engraving, i.e., the creation of a humanly-readable musical score from musical content. This step is fundamental for all applications that inc…
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…
Optical Music Recognition of Jazz Lead Sheets
Juan Carlos Martinez-Sevilla, Francesco Foscarin, Patricia Garcia-Iasci +3
In this paper, we address the challenge of Optical Music Recognition (OMR) for handwritten jazz lead sheets, a widely used musical score type that encodes melody and chords. The ta…