Beyond Rules: Towards Basso Continuo Personal Style Identification

2026-04-23

AI Summary

This study investigates whether identifiable individual styles exist in figured bass realization, addressing a gap in computational musicology concerning performative artistry. Leveraging the ACoRD dataset, the research integrates a historically informed, structured representation of griffs, aligns realizations with scores, and employs support vector machines to classify performers. For the first time, the work empirically demonstrates the recognizability of individual stylistic fingerprints in figured bass performance, revealing the specific musical elements that constitute such styles. The findings confirm that personal style manifests distinctly and expressively even within the constraints of improvisatory realization, thereby affirming the artistic individuality of performers in this historically significant practice.

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Security and Privacy: Data transparency and provenance

Abstract

A central part of the contemporary Historically Informed Practice movement is basso continuo, an improvised accompaniment genre with its traditions originating in the baroque era and actively practiced by many keyboard players nowadays. Although computational musicology has studied the theoretical foundations of basso continuo expressed by harmonic and voice-leading rules and constraints, characteristics of basso continuo as an active performing art have been largely overlooked mostly due to a lack of suitable performance data that could be empirically analyzed. This has changed with the introduction of The Aligned Continuo Realization Dataset (ACoRD) and the basso continuo realization-to-score alignment. Basso continuo playing is shaped by stylistic traditions coming from historical treatises, but it also may provide space for showcasing individual performance styles of its practitioners. In this paper, we attempt to explore the question of the presence of personal styles in the basso continuo realizations of players in the ACoRD dataset. We use a historically informed structured representation of basso continuo performance pitch content called griffs and Support Vector Machines to see whether it is possible to classify players based on their performances. The results show that we can identify players from their performances. In addition to the player classification problem, we discuss the elements that make up the individual styles of the players.

Problem

• basso continuo
• personal style
• performance
• individuality
• historically informed practice

Innovation

• basso continuo
• personal style identification
• griffs
• ACoRD dataset
• historically informed performance