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From the 1 of 12 linked papers with an AI index.

collaborators

12 papers

cs.SD2026

From Prediction to Collaboration: Interactive Symbolic Music Analysis

Emmanouil Karystinaios, Johannes Hentschel, Markus Neuwirth +1

The paper introduces an interactive framework for symbolic Roman‑numeral music analysis that combines strong predictive models with support for constrained completion, revision, an…

cs.SD2026

Dilemmadata: On the Interoperability of Heterogeneous Roman Numeral Datasets

Johannes Hentschel, Emmanouil Karystinaios, Gerhard Widmer +1

In recent years, there has been growing effort to annotate and collect large-scale corpora of Roman numeral analyses in support of data-driven studies in tonal harmony. We introduc…

cs.SD2026

Precise and Simple Audio-to-Score Alignment

Silvan Peter, Patricia Hu, Gerhard Widmer

Audio-to-score alignment is a long-standing challenge in music information retrieval and arguably the most widely applicable alignment task for music research. Alignment algorithms…

cs.SD2026

Multi-Stage Music Source Restoration with BandSplit-RoFormer Separation and HiFi++ GAN

Tobias Morocutti, Emmanouil Karystinaios, Jonathan Greif +1

Music Source Restoration (MSR) targets recovery of original, unprocessed instrument stems from fully mixed and mastered audio, where production effects and distribution artifacts v…

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

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…