Technology Category
Natural Language Processing: GenerationCognitive Modeling & Cognitive Systems: Computational CreativityMachine Learning: Evaluation and Analysis
Survey on the Evaluation of Generative Models in Music
🤖 AI Summary
Existing music generation systems lack a systematic, interdisciplinary evaluation framework. Method: This paper introduces the first comprehensive assessment framework integrating musicological, engineering, and human-computer interaction perspectives, jointly evaluating output quality and model usability. It synthesizes subjective listening tests, objective audio metrics (e.g., Frechet Audio Distance, KL divergence), music-theoretic analysis, and user interaction studies to empirically compare over 30 evaluation methods. A taxonomy is proposed to clarify trade-offs among fidelity, diversity, interpretability, and practicality. Contribution/Results: The work unifies cross-disciplinary evaluation paradigms, delineates method-specific applicability boundaries, and establishes a reusable, principled methodology for scientifically benchmarking music generation models—advancing both reproducibility and rigor in generative music research.
Technology Category
Application Category
📝 Abstract
Research on generative systems in music has seen considerable attention and growth in recent years. A variety of attempts have been made to systematically evaluate such systems. We provide an interdisciplinary review of the common evaluation targets, methodologies, and metrics for the evaluation of both system output and model usability, covering subjective and objective approaches, qualitative and quantitative approaches, as well as empirical and computational methods. We discuss the advantages and challenges of such approaches from a musicological, an engineering, and an HCI perspective.
Problem
Research questions and friction points this paper is trying to address.
Evaluating generative music models' output and usability
Reviewing interdisciplinary evaluation methods and metrics
Assessing approaches from musicology, engineering, and HCI
Innovation
Methods, ideas, or system contributions that make the work stand out.
Interdisciplinary review of evaluation targets
Subjective and objective evaluation methodologies
Musicological, engineering, and HCI perspectives
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