Markus Neuwirth

Emmanouil Karystinaios, Johannes Hentschel, Markus Neuwirth et al.

Automatic symbolic music analysis has made substantial progress, yet existing systems are typically designed for a single mode of use, such as full-score prediction, and therefore do not match the broader range of operations that arise in analysis workflows, including partial completion, local correction, and iterative refinement. As a result, there remains a gap between strong benchmark models and systems that can support interactive analytical use. We present a unified framework for symbolic Roman-numeral (RN) analysis that narrows this gap by combining strong predictive performance with direct support for constrained completion and revision. The method is designed to provide a practical trade-off between accuracy and interactive responsiveness by computing expensive pretrained representations once and reusing them during iterative refinement, making powerful pretrained models more amenable to interactive settings. It supports complete score analysis, targeted revision of existing labels, and inference of missing annotations from partial context through a shared modeling framework. Experiments on Dilemmadata, the largest and most heterogeneous benchmark of its kind, show that the proposed approach is a strong RN-analysis baseline while also supporting masked completion from partial labels. Together with a prototype interface for multi-level candidate inspection and editing, these results position automatic RN analysis not only as a prediction problem, but also as a foundation for future interactive tools for music analysis.

Johannes Hentschel, Emmanouil Karystinaios, Gerhard Widmer et al.

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 introduce dilemmadata, the first resource to reconcile two major collections, the AugmentedNet Dataset (AN) and the Distant Listening Corpus (DLC), making them interoperable through a shared note-wise TSV schema. The reconciliation confronts four families of dilemmata: annotation-standard (the two encode the same musical fact differently in terms of vocabulary size, syntax, conventions for chord extensions, inventory of special chord functions), representational (what counts as a row, and which information survives the conversion), toolchain (incompatible Python ecosystems built around music21 vs. ms3+dimcat), and curatorial (which pieces to include, exclude, or retain twice). We resolve each by deliberately transforming, augmenting, and omitting information, formalising the mismatches, preserving musical semantics, and flagging transformations that may subtly affect annotation fidelity. Consistency checks and qualitative inspections offer a preliminary assessment of post-conversion validity and a basis for critiquing the theoretical assumptions embedded in each original standard. After removing duplicates and merging the two collections, the resulting dilemmadata (1,621 pieces and aprox. 2.8 M note-wise annotations) is the largest homogeneous Roman-numeral corpus currently available, albeit far from perfect. Crucially, we retain 84 pieces common to both corpora under each of their original analyses, yielding a shared reference set in which two equally legitimate analytical traditions can be compared note-for-note over identical musical material. Released on Zenodo, dilemmadata supports interoperability, comparative harmonization modeling, and future refinement of Roman-numeral encoding standards.

Emmanouil Karystinaios, Johannes Hentschel, Markus Neuwirth et al.

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 AnalysisGNN, a novel graph neural network framework that leverages a data-shuffling strategy with a custom weighted multi-task loss and logit fusion between task-specific classifiers to integrate heterogeneously annotated symbolic datasets for comprehensive score analysis. We further integrate a Non-Chord-Tone prediction module, which identifies and excludes passing and non-functional notes from all tasks, thereby improving the consistency of label signals. Experimental evaluations demonstrate that AnalysisGNN achieves performance comparable to traditional static-dataset approaches, while showing increased resilience to domain shifts and annotation inconsistencies across multiple heterogeneous corpora.