What does this research mean for the field?

The evolution of music generation techniques has progressed from single-modal to cross-modal and is now advancing towards multi-modal fusion. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The research aims to systematically review the evolution of music generation methods from different modal perspectives.

March 7, 2026

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

Key Points

The research aims to systematically review the evolution of music generation methods from different modal perspectives.
Reviewed representation methods for audio, symbolic, text, and visual data.
Organized music generation techniques by single-modal, cross-modal, and multi-modal categories.
Compiled relevant datasets and evaluation methodologies.
Discussed challenges in modal fusion and data scarcity.
Identified key approaches in music generation across modalities.
Outlined significant challenges in the current methodologies.
Suggested future research directions to enhance music generation techniques.

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.

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