Survey on the Evaluation of Generative Models in Music

IF 28 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Alexander Lerch, Claire Arthur, Nick Bryan-Kinns, Corey Ford, Qianyi Sun, Ashvala Vinay
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引用次数: 0

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.
音乐生成模型评价综述
近年来,音乐生成系统的研究得到了相当大的关注和发展。为了系统地评价这些系统,已经进行了各种尝试。我们对系统输出和模型使用评估的共同评估目标、方法和度量进行了跨学科的回顾,涵盖了主观和客观方法、定性和定量方法,以及经验和计算方法。我们从音乐学、工程学和HCI的角度来研究这些方法的优点和局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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