Beyond Bars: Distribution of Edit Operations in Historical Prints

2025-09-16

AI Summary

Full-score digitization for comparative musicological corpus studies is time-consuming and inefficient. Method: This paper proposes a novel approach based on representative measure sampling—replacing complete-score digitization with strategically selected measures—and systematically evaluates three statistical sampling strategies for their efficacy in capturing editorial variants, using Beethoven’s Op. 119 as a cross-version case study. Contribution/Results: Hierarchical random sampling demonstrates superior statistical robustness and efficiency in identifying the distribution of editorial operations. The method substantially reduces manual labor and temporal investment while enabling large-scale, reproducible empirical research on musical editing practices. It provides a scalable methodological framework for nineteenth-century musical text comparison and advances historical musicology toward a data-driven paradigm.

Technology Category

Search and Optimization: Sampling/Simulation-based SearchData Mining & Knowledge Management: Data CompressionHumans and AI: Crowd Sourcing and Human Computation

Application Category

Web Mining and Content Analysis: Robustness and generalizability of Web mining methodsEconomics, Online Markets and Human Computation: Data quality aspects of human-annotated datasetsSecurity and Privacy: Large-scale security measurements

Abstract

In this paper, we present a method for conducting comparative corpus studies in musicology that reduces the time-consuming digitization process. Instead of encoding whole corpora of musical sources, we suggest sampling bars from these sources. We address the challenge of selecting representative samples and evaluate three different sampling methods. We used Beethoven's Bagatelles Op. 33 as a case study to find the method that works best in finding samples representative with respect to differences. We believe that this approach offers significant value to musicological research by enabling large-scale analyses and thereby statistically sound results. Moreover, we believe our work to be a valuable step toward understanding nineteenth-century editorial practices and enriching the field of scholarly editing of historical musical works.

Problem

• Reducing time-consuming digitization in musicology studies
• Selecting representative samples from musical sources
• Evaluating sampling methods for comparative corpus analysis

Innovation

• Sampling bars from musical sources
• Evaluating three different sampling methods
• Using Beethoven's Bagatelles as case study