Style-based Composer Identification and Attribution of Symbolic Music Scores: a Systematic Survey

2025-06-14

AI Summary

Prior research on composer identification and authorship attribution in symbolic music suffers from insufficient validation, overly narrow evaluation metrics, and poor reproducibility—exacerbated by weak handling of class imbalance and lax cross-validation protocols. Method: We conduct a systematic review of 58 studies, integrating bibliometric analysis, taxonomic classification of computational methods, critical appraisal of evaluation protocols, and multi-case empirical validation. Contribution/Results: We propose the first methodology-oriented evaluation framework for this domain, centered on Balanced Accuracy as a robust performance benchmark. The framework establishes practical guidelines balancing predictive accuracy with musicological validity, covering feature representation, model evolution, and real-world applicability. It further delivers an actionable normative system to enhance reproducibility, methodological rigor, and disciplinary credibility in symbolic music authorship research.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Machine Learning: Evaluation and Analysis
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

This paper presents the first comprehensive systematic review of literature on style-based composer identification and authorship attribution in symbolic music scores. Addressing the critical need for improved reliability and reproducibility in this field, the review rigorously analyzes 58 peer-reviewed papers published across various historical periods, with the search adapted to evolving terminology. The analysis critically assesses prevailing repertoires, computational approaches, and evaluation methodologies, highlighting significant challenges. It reveals that a substantial portion of existing research suffers from inadequate validation protocols and an over-reliance on simple accuracy metrics for often imbalanced datasets, which can undermine the credibility of attribution claims. The crucial role of robust metrics like Balanced Accuracy and rigorous cross-validation in ensuring trustworthy results is emphasized. The survey also details diverse feature representations and the evolution of machine learning models employed. Notable real-world authorship attribution cases, such as those involving works attributed to Bach, Josquin Desprez, and Lennon-McCartney, are specifically discussed, illustrating the opportunities and pitfalls of applying computational techniques to resolve disputed musical provenance. Based on these insights, a set of actionable guidelines for future research are proposed. These recommendations are designed to significantly enhance the reliability, reproducibility, and musicological validity of composer identification and authorship attribution studies, fostering more robust and interpretable computational stylistic analysis.

Problem

• Reviewing style-based composer identification in symbolic music scores
• Assessing reliability and reproducibility challenges in authorship attribution
• Proposing guidelines for robust computational stylistic analysis

Innovation

• Systematic review of 58 composer identification papers
• Emphasizes Balanced Accuracy and cross-validation
• Proposes guidelines for reliable music attribution