Time to Align! Modelling Musical Timelines for Music Information Retrieval and Digital Musicology
HENTSCHEL J, BERNDT A, CANCINO-CHACÓN C, DIXON S, FOO A, GOTHAM M, HU P, KÖSTER M, MARTINS F, MAURO D, Müller M, NEUWIRTH M, PACHA A, PAGE K, PETER S, POLYAKOV E, PUGIN L, WEIGL D, WEIß C, WIDMER G (2026)
Publication Type: Journal article
Publication year: 2026
Journal
Book Volume: 9
Pages Range: 384-404
Journal Issue: 1
DOI: 10.5334/tismir.296
Abstract
Aligning musical data – matching corresponding events across different representations of the same music – is central to Music Information Retrieval and Digital Musicology. Datasets span graphical representations (images, scans), logical encodings (scores, MIDI) and physical recordings (audio, video, sensor data), each imposing its own coordinate system for musical time. Integrating them requires costly alignment, but results are typically stored in ad hoc formats that are poorly documented and difficult to share and reuse. To address this, we present TimeToAlign!, a cross-domain model for musical timelines and their alignments that organises time-related data into continuous and discrete variants across three temporal domains – graphical, logical and physical – connected by typed conversion maps and explicit match claims. In addition to describing the model, we demonstrate it through five concrete use cases of increasing complexity: (1) aligning a historical piano roll with a digital score, (2) modelling flow control in a school song arrangement, (3) transferring annotations between graphical analyses, (4) propagating harmonic labels across hundreds of multimodal timelines of a Beethoven string quartet and (5) encoding the conceptual and temporal relationships in the genesis of a rock song. The model is implemented as timetoalign, an open-source Python library with typed timelines, loaders for common formats (such as audio formats, MusicXML or CSV) and conversion maps. Runnable tutorial and how-to notebooks, a documentation homepage and a collection of real-world data from diverse repertoires accompany the library.
Authors with CRIS profile
Involved external institutions
Anton Bruckner Privatuniversität
Austria (AT)
Universität Paderborn
Germany (DE)
Johannes Kepler Universität (JKU) Linz
Austria (AT)
Queen Mary University of London
United Kingdom (GB)
King’s College London
United Kingdom (GB)
University of Music and Performing Arts, Vienna / Universität für Musik und darstellende Kunst Wien
Austria (AT)
Technische Universität Wien / Vienna University of Technology
Austria (AT)
University of Oxford
United Kingdom (GB)
Hochschule für Musik FRANZ LISZT Weimar
Germany (DE)
Universität Bern
Switzerland (CH)
Julius-Maximilians-Universität Würzburg
Germany (DE)
Austria (AT)
Universität Paderborn
Germany (DE)
Johannes Kepler Universität (JKU) Linz
Austria (AT)
Queen Mary University of London
United Kingdom (GB)
King’s College London
United Kingdom (GB)
University of Music and Performing Arts, Vienna / Universität für Musik und darstellende Kunst Wien
Austria (AT)
Technische Universität Wien / Vienna University of Technology
Austria (AT)
University of Oxford
United Kingdom (GB)
Hochschule für Musik FRANZ LISZT Weimar
Germany (DE)
Universität Bern
Switzerland (CH)
Julius-Maximilians-Universität Würzburg
Germany (DE)
How to cite
APA:
HENTSCHEL, J., BERNDT, A., CANCINO-CHACÓN, C., DIXON, S., FOO, A., GOTHAM, M.,... WIDMER, G. (2026). Time to Align! Modelling Musical Timelines for Music Information Retrieval and Digital Musicology. Transactions of the International Society for Music Information Retrieval, 9(1), 384-404. https://doi.org/10.5334/tismir.296
MLA:
HENTSCHEL, JOHANNES, et al. "Time to Align! Modelling Musical Timelines for Music Information Retrieval and Digital Musicology." Transactions of the International Society for Music Information Retrieval 9.1 (2026): 384-404.
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