Toward Musicologically-Informed Retrieval: Enhancing MEI with Computational Metadata

We present a metadata enrichment framework for Music Encoding Initiative (MEI) files, featuring mid- to higher-level multimodal features to support content-driven (similarity) retrieval with semantic awareness across large collections. While traditional metadata captures basic bibliographic and structural elements, it often lacks the depth required for advanced retrieval tasks that rely on musical phrases, form, key or mode, idiosyncratic patterns, and textual topics. To address this, we propose a system that fosters the computational analysis and edition of MEI encodings at scale. Inserting extended metadata derived from computational analysis and heuristic rules lays the groundwork for more nuanced retrieval tools. A batch environment and a lightweight JavaScript web-based application propose a complementary workflow by offering large-scale annotations and an interactive environment for reviewing, validating, and refining MEI files' metadata. Development is informed by user-centered methodologies, including consultations with music editors and digital musicologists, and has been co-designed in the context of orally transmitted folk music traditions, ensuring that both the batch processes and interactive tools align with scholarly and domain-specific needs.