Book chapter, 2026
MishMash Projects
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Type
Book chapter
Report
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Report, 2026
COST Action EarlyMuse 2022-2026. Six Reports on Musicology in Europe
This volume gathers the six reports produced between 2025 and 2026 within COST Action CA21161, A New Ecosystem of Early Music Studies (EarlyMuse), the first COST Action ever devoted to historical musicology. Running from September 2022 to October 2026, the Action brought together close to three hundred researchers from some forty countries in five working groups: Education, Sources, Publications, Performances and Policies. Each report was drafted, discussed and published separately, with its own editorial team, corpus, method and primary audience. Assembled here, they describe a single object from six angles: not "early music" as a repertory, but the ecosystem in which musical scholarship is now produced: the departments and conservatoires that train and employ musicologists, the doctoral frameworks that certify their work, the libraries and archives that hold the sources, the digital infrastructures and computational tools that mediate access to them, the European funding architectures that decide which questions become fundable, and the professional and public worlds in which musical knowledge circulates. The six reports cover: musicology as data science and heritage science; the mapping of musicology in European higher education and research institutions; musicology and artificial intelligence; historical musicology and European research infrastructures and initiatives (EOSC, ECCCH/ECHOES, the Data Space for Cultural Heritage, DARIAH, E-RIHS); doctoral-level artistic research in music; and musicology within Horizon Europe co-funded partnerships, notably Resilient Cultural Heritage and the emerging Virtual Worlds partnership. Taken together, they document a growing mismatch between the intellectual vitality of the field and the fragility of the structures through which it is taught, documented, funded and made visible. Recurrent findings include the statistical invisibility of musicology in OECD, Eurostat and UNESCO classifications; the institutional cost of methodological pluralism; and the claim that the field is under-positioned rather than under-qualified for the European agendas now being written. The volume is neither a state of the art nor a synthesis nor a manifesto, but a handover document, a description of the terrain, closing with recommendations addressed to institutions, national funders, European bodies, learned societies and individual researchers.
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Report, 2026
Formation of a Special Interest Group on Linked Metadata and Interoperability in Musicology: Report and Results from the International Musicology Workshop Held Between NFDI4Culture, musica*, RISM and the MishMash Centre for AI and Creativity
The report presents the results and the outcome of an international musicology workshop held at the Academy of Sciences and Literature | Mainz on 7th- 8th May 2026. The workshop brought together for the first time members of musica* – Consortium en musicologie numérique from the French digital humanities infrastructure project Huma-Num and musicologists from NFDI4Culture, the German Consortium for Research Data on Material and Immaterial Cultural Heritage, as well as experts from the RISM Digital Centre of the Répertoire International des Sources Musicales (RISM), based at the Swiss National Library in Basel, and from the MishMash Centre for AI and Creativity, a consortium of Norwegian research institutions focusing on the application of artificial intelligence in the cultural sector. Furthermore, musicologists and developers from the Specialized Information Service (FID) for Musicology (musiconn) and the Centre for Digital Music Documentation (CDMD) were present. At the workshop, the group established a new Special Interest Group on Linked Metadata and Interoperability in Musicology. This group will lay the groundwork for future collaborative efforts with a focus on improving the interoperability and alignment of metadata between the aforementioned projects and consortia. The first steps in this process are documented in this report.
Performing arts
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Performing arts, 2026
Hardingfele and AI
Skurdalsbruri played by Joan Gatti, with real-time visualisation of the music transcription and melodic analysis
Conference
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Conference poster, 2026
Towards automated transcription of Hardanger fiddle performances
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Conference poster, 2026
Hybrid AI for Expert-Assisted Transcription of Norwegian Hardanger Fiddle Music
The traditional Norwegian Hardanger fiddle repertoire is a rich oral heritage. Existing score transcriptions are scarce and rarely capture essential performance features, particularly ornamentation, microtonal intonation, two-string textures, and systematic beat unevenness. We develop an innovative AI-assisted transcription framework producing detailed, performance-aligned MIDI representations that preserve fine- grained timing and pitch. The neural front-end adapts a state-of-the-art deep-learning model for polyphonic music transcription to our small but growing dataset of expert annotations. It progressively learns from expert corrections, providing increasingly robust results, as reflected by a rising F1 score. The model is complemented by signal processing, musicological knowledge, and cognitive principles addressing repertoire-specific challenges such as non-standard tuning, sympathetic-string resonances, sustained drones, and symbolic note offsets. Acoustic analysis refines pitch beyond the conventional twelve-tone MIDI grid, while tune-specific pitch distributions organise the performed notes into musically meaningful categories. These results are transformed into a new score representation proportionally aligned with the recording. Colours make stable “micromodal” pitch categories visible and highlight ambiguous notes or pitches lying outside those categories. This makes the score useful not only for reading and performance, but also for checking AI output, exploring intonation and texture, and identifying recurring patterns. This expert-in-the-loop workflow drastically reduces transcription correction time, with the aim of progressing towards full automation. The resulting transcriptions and scores will support performance, education, archive exploration, corpus research, and future AI methods for musical heritage. This original hybrid AI approach also demonstrates the limitations of purely machine-learning approaches to music transcription.
Policy report
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Policy report, 2026
Musicology and Artificial Intelligence
This report provides an overview of Artificial Intelligence in European musicological research, addressed to musicologists, librarians, archivists, curators, educators, research-policy officers, infrastructure coordinators, and tool developers. Moving from conceptual foundations to institutional implications, it maps the affordances and limitations of AI-based models and tools for the discipline, while situating them within the European normative landscape defined by the ALLEA European Code of Conduct for Research Integrity, the EU Artificial Intelligence Act, and the UNESCO Recommendation on the Ethics of Artificial Intelligence. The six complementary sections successively address: (1) methodological foundations, clarifying notions of model, tool, and critical posture, and surveying audio-, image-, encoding-, and language-model-based applications such as automatic music transcription, optical music recognition, score-audio alignment, automated analysis, reconstruction, and LLM prompting; (2) data infrastructure, covering FAIR/CARE governance, ontologies and persistent identifiers, European federated infrastructures, auditory digital twins, and risks of fragmentation, representational bias, quality variability, and copyright; (3) a continuation of the disciplinary stakes involved in source and corpus processing; (4) musicological education in the age of AI, including curricular redesign, AI literacy, and design models such as the Two-Lane Approach; (5) professional practices and responsibilities, examining scholarly credibility, intellectual property, academic integrity frameworks, cultural and creative industry collaborations, institutional and legal risks, gender equality, and lessons from organised labour; and (6) best practices, articulating methodological pluralism against technological determinism and formalising principles of ethical reflexivity, human oversight, explainability, reproducibility, and accountability.
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Policy report, 2025
Musicology as Data Science and Heritage Science
Book of abstracts
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Book of abstracts, 2026
DAIM 2026: Symposium on Digital and AI Musicology
This report presents extended summaries of the presentations and discussions at the DAIM 2026 Symposium on Digital and AI Musicology, held on 8–9 June 2026 at the National Library of Norway and the University of Oslo. Organized by RITMO, the MishMash Centre for AI and Creativity, and the National Library of Norway, the symposium brought together the COST Action EarlyMuse and MishMash Work Package 6 on AI for Cultural Heritage. The contributions examine how computational methods and artificial intelligence can support the preservation, transcription, analysis, discovery, and interconnection of musical heritage. Topics include digital music collections, Optical Music Recognition, audio transcription, chant and Sámi yoik, musical similarity and pattern discovery, Linked Open Data, metadata standards, and sustainable research infrastructures. Discussions address scholarly validation, provenance, cultural context, and the rights of creators and communities. Compiled and edited by Olivier Lartillot, the report synthesizes presentation abstracts, slides, and recorded talks. It includes introductions to both days, presentation and panel summaries, links to related resources, contributor biographies, and the symposium programme, alongside a tribute to Eleanor Selfridge-Field.
Textbook
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Textbook, 2025
Annotemus User’s Guide