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

TL;DR

This survey-based study evaluates current computational musicology tools, identifying gaps between capabilities and user needs across various domains to guide future development and improve research effectiveness.

Contribution

It provides a comprehensive assessment of existing tools based on practitioner surveys, highlighting limitations and areas for improvement in computational musicology.

Findings

Significant gaps between tool capabilities and user needs

Limited satisfaction with current analytical tools

Identified priorities for future tool development

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.