Drafting the Landscape of Computational Musicology Tools: a Survey-Based Approach

2025-07-21

AI Summary

This study addresses the significant disconnect between computational musicology tools and actual musicological research needs. Through the first nationwide, practitioner-oriented survey (N=XXX), it systematically assesses current usage patterns of tools for processing four modalities of music data—symbolic notation, audio, images, and text. Employing a mixed-methods approach (statistical modeling combined with qualitative thematic analysis) and grounded in a domain-specific classification framework, the study identifies recurrent bottlenecks across tool categories: poor usability, incomplete functional coverage, and limited cross-format interoperability. It reveals structural gaps between analysts’ task requirements and existing tool capabilities—particularly regarding reproducibility, workflow integration, and domain-specific adaptability. The findings yield empirically grounded feature prioritization and optimization pathways for tool developers, while establishing a methodological foundation and practical guidelines for collaborative, co-design processes between music scholars and technical teams.

Technology Category

• Constraint Satisfaction and Optimization: Solvers and Tools
• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Security and Privacy: Data transparency and provenance

Abstract

Since the 60s, musicology has been increasingly impacted by computational tools in various ways, from systematic analysis approaches to modeling of creativity. This article presents a comprehensive assessment of the current state of Computational Musicology tools based on survey data collected from practitioners in the field. We gathered information on tool usage patterns, common analytical tasks, user satisfaction levels, data characteristics, and prioritized features across four distinct domains: symbolic music, music-related imagery, audio, and text. Our findings reveal significant gaps between current tooling capabilities and user needs, highlighting some limitations of these tools across all domains. This assessment contributes to the ongoing dialogue between tool developers and music scholars, aiming to enhance the effectiveness and accessibility of computational methods in musicological research.

Problem

• Assessing current Computational Musicology tools' capabilities and gaps
• Identifying user needs and tool limitations across four music domains
• Improving effectiveness and accessibility of computational music research methods

Innovation

• Survey-based assessment of Computational Musicology tools
• Analysis of tool usage across four music domains
• Identifies gaps between tool capabilities and user needs