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

• URL: http://arxiv.org/abs/2506.12440v1
• Date: Sat, 14 Jun 2025 10:34:07 GMT
• Title: Style-based Composer Identification and Attribution of Symbolic Music Scores: a Systematic Survey
• Authors: Federico Simonetta,
• Abstract summary: This paper presents the first systematic review of literature on style-based composer identification and authorship attribution in symbolic music scores.<n>It addresses the critical need for improved reliability and rigorously analyzes 58 peer-reviewed papers published across various historical periods.<n>It reveals that a substantial portion of existing research suffers from inadequate validation protocols and an over-reliance on simple accuracy metrics.
• Score: 0.9065034043031668
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• In-depth Research Impact Summarization through Fine-Grained Temporal Citation Analysis [52.42612945266194]
  We propose a new task: generating nuanced, expressive, and time-aware impact summaries.<n>We show that these summaries capture both praise (confirmation citations) and critique (correction citations) through the evolution of fine-grained citation intents.
  arXiv  Detail & Related papers  (2025-05-20T19:11:06Z)
• exHarmony: Authorship and Citations for Benchmarking the Reviewer Assignment Problem [11.763640675057076]
  We develop a benchmark dataset for evaluating the reviewer assignment problem without needing explicit labels.<n>We benchmark various methods, including traditional lexical matching, static neural embeddings, and contextualized neural embeddings.<n>Our results indicate that while traditional methods perform reasonably well, contextualized embeddings trained on scholarly literature show the best performance.
  arXiv  Detail & Related papers  (2025-02-11T16:35:04Z)
• Causality can systematically address the monsters under the bench(marks) [64.36592889550431]
  Benchmarks are plagued by various biases, artifacts, or leakage.<n>Models may behave unreliably due to poorly explored failure modes.<n> causality offers an ideal framework to systematically address these challenges.
  arXiv  Detail & Related papers  (2025-02-07T17:01:37Z)
• Explaining the Unexplained: Revealing Hidden Correlations for Better Interpretability [1.8274323268621635]
  Real Explainer (RealExp) is an interpretability method that decouples the Shapley Value into individual feature importance and feature correlation importance.<n>RealExp enhances interpretability by precisely quantifying both individual feature contributions and their interactions.
  arXiv  Detail & Related papers  (2024-12-02T10:50:50Z)
• A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [55.33653554387953]
  Pattern Analysis and Machine Intelligence (PAMI) has led to numerous literature reviews aimed at collecting and fragmented information.<n>This paper presents a thorough analysis of these literature reviews within the PAMI field.<n>We try to address three core research questions: (1) What are the prevalent structural and statistical characteristics of PAMI literature reviews; (2) What strategies can researchers employ to efficiently navigate the growing corpus of reviews; and (3) What are the advantages and limitations of AI-generated reviews compared to human-authored ones.
  arXiv  Detail & Related papers  (2024-02-20T11:28:50Z)
• Interpretable Automatic Fine-grained Inconsistency Detection in Text Summarization [56.94741578760294]
  We propose the task of fine-grained inconsistency detection, the goal of which is to predict the fine-grained types of factual errors in a summary. Motivated by how humans inspect factual inconsistency in summaries, we propose an interpretable fine-grained inconsistency detection model, FineGrainFact.
  arXiv  Detail & Related papers  (2023-05-23T22:11:47Z)
• Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach [77.61131357420201]
  We conduct a thorough and rigorous study on fairness disparities in peer review with the help of large language models (LMs) We collect, assemble, and maintain a comprehensive relational database for the International Conference on Learning Representations (ICLR) conference from 2017 to date. We postulate and study fairness disparities on multiple protective attributes of interest, including author gender, geography, author, and institutional prestige.
  arXiv  Detail & Related papers  (2022-11-07T16:19:42Z)
• TraSE: Towards Tackling Authorial Style from a Cognitive Science Perspective [4.123763595394021]
  Authorship attribution experiments with over 27,000 authors and 1.4 million samples in a cross-domain scenario resulted in 90% attribution accuracy. A qualitative analysis is performed on TraSE using physical human characteristics, like age, to validate its claim on capturing cognitive traits.
  arXiv  Detail & Related papers  (2022-06-21T19:55:07Z)
• A Sentiment Analysis Dataset for Trustworthiness Evaluation [22.734197353027632]
  Deep learning models are often criticized to be untrustworthy due to the black-box problem. We release a novel and well-annotated sentiment analysis dataset to evaluate robustness and interpretability.
  arXiv  Detail & Related papers  (2021-08-30T11:58:16Z)
• Counterfactual Representation Learning with Balancing Weights [74.67296491574318]
  Key to causal inference with observational data is achieving balance in predictive features associated with each treatment type. Recent literature has explored representation learning to achieve this goal. We develop an algorithm for flexible, scalable and accurate estimation of causal effects.
  arXiv  Detail & Related papers  (2020-10-23T19:06:03Z)
• Accurate and Robust Feature Importance Estimation under Distribution Shifts [49.58991359544005]
  PRoFILE is a novel feature importance estimation method. We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
  arXiv  Detail & Related papers  (2020-09-30T05:29:01Z)
• A Framework for Evaluation of Machine Reading Comprehension Gold Standards [7.6250852763032375]
  This paper proposes a unifying framework to investigate the present linguistic features, required reasoning and background knowledge and factual correctness. The absence of features that contribute towards lexical ambiguity, the varying factual correctness of the expected answers and the presence of lexical cues, all of which potentially lower the reading comprehension complexity and quality of the evaluation data.
  arXiv  Detail & Related papers  (2020-03-10T11:30:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.