A Survey on Music Generation from Single-Modal, Cross-Modal, and Multi-Modal Perspectives: Data, Methods, and Challenges
Shuyu Li, Shulei Ji, Zihao Wang, Songruoyao Wu, Jiaxing Yu, Kejun Zhang·April 01, 2025
Summary
Summary in English: This text surveys multi-modal music generation, integrating audio, scores, and text. It discusses techniques like GANs, diffusion models, and autoregressive models, using datasets such as LMD, MusicNet, MAESTRO, and ASAP for score-audio alignment. The MAESTRO dataset is the largest. Text-music datasets like MusicCaps balance genres. Future research focuses on multi-modal fusion, alignment, data, and evaluation. The MidiCaps dataset addresses limitations of MusicCaps, offering precise alignment and temporal accuracy. Visual-Music datasets connect music with performance videos, aiding dance-music study. Recent advancements include deep generative models for raw audio, neural Hawkes processes, controllable text-to-music generation, and large-scale MIDI datasets. Notable works cover efficient word representation estimation, conditional generative adversarial networks, symbolic music generation with diffusion models, and music analysis datasets. The text highlights improvements in neural network architectures for enhancing language understanding and music generation capabilities.
Introduction
Background
Overview of multi-modal music generation
Objective
To explore techniques, datasets, and future research directions in multi-modal music generation
Techniques in Multi-Modal Music Generation
Generative Adversarial Networks (GANs)
GANs for audio synthesis and score generation
Diffusion Models
Diffusion models for music generation
Autoregressive Models
Autoregressive models for sequential music generation
Datasets for Multi-Modal Music Generation
LMD (Large Music Dataset)
Description and usage in multi-modal music generation
MusicNet
Features and applications in music analysis
MAESTRO
The largest dataset for score-audio alignment
ASAP (Audio-Score Alignment and Parsing)
Overview and significance in multi-modal music generation
Future Research Directions
Multi-Modal Fusion
Techniques for integrating audio, scores, and text
Alignment and Data
Challenges and advancements in score-audio alignment
Evaluation Methods
Metrics and frameworks for assessing multi-modal music generation
Addressing Limitations and Enhancements
MidiCaps Dataset
Improvements over MusicCaps in alignment and temporal accuracy
Visual-Music Datasets
Connecting music with performance videos for dance-music study
Recent Advancements
Deep generative models for raw audio, neural Hawkes processes, and controllable text-to-music generation
Large-Scale MIDI Datasets
Enhancing music generation with extensive MIDI data
Notable Works
Efficient Word Representation Estimation
Techniques for better language understanding in music generation
Conditional Generative Adversarial Networks (CGANs)
Enhancing music generation with conditional inputs
Symbolic Music Generation with Diffusion Models
Using diffusion models for symbolic music creation
Music Analysis Datasets
Supporting research and development in music analysis
Improvements in Neural Network Architectures
Enhancing Language Understanding
Architectural innovations for better music representation
Music Generation Capabilities
Advancements in generating complex musical structures
Basic info
papers
multimedia
sound
artificial intelligence
Advanced features