Survey on the Evaluation of Generative Models in Music
Sound
2025-09-23 v4 Artificial Intelligence
Machine Learning
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 present an interdisciplinary review of the common evaluation targets, methodologies, and metrics for the evaluation of both system output and model use, covering subjective and objective approaches, qualitative and quantitative approaches, as well as empirical and computational methods. We examine the benefits and limitations of these approaches from a musicological, an engineering, and an HCI perspective.
Keywords
Cite
@article{arxiv.2506.05104,
title = {Survey on the Evaluation of Generative Models in Music},
author = {Alexander Lerch and Claire Arthur and Nick Bryan-Kinns and Corey Ford and Qianyi Sun and Ashvala Vinay},
journal= {arXiv preprint arXiv:2506.05104},
year = {2025}
}
Comments
Accepted paper submitted to ACM CSUR on 12-Sep-2025, original manuscript submitted on 26-Jun-2024