Community Based Music Information Retrieval: A Case Study of Digitizing Historical Klezmer Manuscripts from Kyiv. Issue 1 (30th November 2022)

• Record Type: Journal Article
• Authors: Malin, Yonatan; Crowder, Christina; Byom, Clara; Shanahan, Daniel
• Is Part Of: Transactions of the International Society for Music Information Retrieval. Volume 5:Issue 1(2022)
• Page Start: 208
• Page End: 221
• Publication Date: 2022-11-30
• Subjects: heritage music -- klezmer -- Jewish music -- crowdsourcing -- computational ethnomusicology; 025
• DOI: 10.5334/tismir.135
• Languages: English
• ISSNs: 2514-3298

Abstract

In this article we provide a case study in the datafication of historical handwritten manuscripts, which diversifies the repertoire, approaches, demographics, and institutional partnerships of MIR. The Kiselgof-Makonovetsky Digital Manuscript Project (KMDMP) is a community-based project to digitize music and text, teach, and make music from facsimiles of manuscripts held by the Vernadsky National Library of Ukraine. The corpus comprises 850 high-resolution photographs of handwritten music manuscripts and catalog pages, with a total of around 1, 300 melodies. Much of the music was collected by pioneering Belarusian ethnographer Zusman Kiselgof among Jewish communities in the 'Pale of Settlement' (mostly in modern Ukraine and Belarus) during the An-Ski Expeditions of 1912–1914. The repertoire is mixed, combining typical Jewish dance and non-dance genres, European society and folklore dance music, and a relatively small quantity of songs and liturgical chant settings. The project simultaneously encodes music in formats accessible to computational musicology and enhances a creative musical community and deeply valued heritage. We introduce the project in dialogue with a recent article by Georgina Born on diversity in the field of MIR; present the material, issues for datafication, and results thus far; describe project elements that enhance musical community; demonstrate the diversity of participants with respect to age, gender, nationality, and profession; outline implications for MIR and computational ethnomusicology; and suggest new funding models and partnerships in support of cultural heritage documentation, preservation, continuity, and analysis.