A Survey on Music Generation from Single-Modal, Cross-Modal, and Multi-Modal Perspectives

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Authors: Shuyu Li, Shulei Ji, Zihao Wang, Songruoyao Wu, Jiaxing Yu, Kejun Zhang
Cited by: 1
References: 61

Abstract

With the rapid development of artificial intelligence, music generation has evolved from single-modal to cross-modal approaches and is gradually moving toward multi-modal fusion. This survey systematically reviews this developmental trajectory. The discussion begins with the representation methods for key modalities, including audio, symbolic, text, and visual data. Music generation techniques are then organized across single-modal, cross-modal, and multi-modal settings. In addition, key datasets and evaluation methodologies relevant to these tasks are compiled. Finally, the survey discusses core challenges in the field, including modal fusion, data scarcity, and evaluation frameworks, and outlines potential directions for future research.

Fields of Study & Subjects

Music Technology and Sound Studies Music and Audio Processing Generative Adversarial Networks and Image Synthesis

Publication details

Journal / source
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Volume
58
Issue
11
Pages
1–38
Publication date
5 March 2026
Language
EN