1 citations · 2 across the 9 of their papers we have counts for
11 papers
MusicLayout: Explicit Structural Planning for Controllable Text-to-Music Generation
Shuyu Li, Kejun Zhang, Jiahe Lei +5
Text-to-music generation has advanced rapidly, but current systems still rely primarily on global text prompts, leaving the structural organization of generated music implicit and…
Diff-V2M: A Hierarchical Conditional Diffusion Model with Explicit Rhythmic Modeling for Video-to-Music Generation
Shulei Ji, Zihao Wang, Jiaxing Yu +4
Video-to-music (V2M) generation aims to create music that aligns with visual content. However, two main challenges persist in existing methods: (1) the lack of explicit rhythm mode…
SSIMuse: Assessing Data Replication in Symbolic Music via an Adapted Structural Similarity Index Measure
Shulei Ji, Zihao Wang, Le Ma +2
AI-generated music may inadvertently replicate training samples and raise plagiarism concerns. Similarity measures can quantify such replication, thereby offering supervision and g…
Losing is for Cherishing: Data Valuation Based on Machine Unlearning and Shapley Value
Le Ma, Shirao Yang, Zihao Wang +4
The proliferation of large models has intensified the need for efficient data valuation methods to quantify the contribution of individual data providers. Traditional approaches, s…
A Survey on Music Generation from Single-Modal, Cross-Modal, and Multi-Modal Perspectives
Shuyu Li, Shulei Ji, Zihao Wang +3
Multi-modal music generation, using multiple modalities like text, images, and video alongside musical scores and audio as guidance, is an emerging research area with broad applica…
A Comprehensive Survey on Generative AI for Video-to-Music Generation
Shulei Ji, Songruoyao Wu, Zihao Wang +2
The burgeoning growth of video-to-music generation can be attributed to the ascendancy of multimodal generative models. However, there is a lack of literature that comprehensively…
MetaBGM: Dynamic Soundtrack Transformation For Continuous Multi-Scene Experiences With Ambient Awareness And Personalization
Haoxuan Liu, Zihao Wang, Haorong Hong +5
This paper introduces MetaBGM, a groundbreaking framework for generating background music that adapts to dynamic scenes and real-time user interactions. We define multi-scene as va…
MuDiT & MuSiT: Alignment with Colloquial Expression in Description-to-Song Generation
Zihao Wang, Haoxuan Liu, Jiaxing Yu +3
Amid the rising intersection of generative AI and human artistic processes, this study probes the critical yet less-explored terrain of alignment in human-centric automatic song co…
SaMoye: Zero-shot Singing Voice Conversion Model Based on Feature Disentanglement and Enhancement
Zihao Wang, Le Ma, Yongsheng Feng +3
Singing voice conversion (SVC) aims to convert a singer's voice to another singer's from a reference audio while keeping the original semantics. However, existing SVC methods can h…
MuChin: A Chinese Colloquial Description Benchmark for Evaluating Language Models in the Field of Music
Zihao Wang, Shuyu Li, Tao Zhang +6
The rapidly evolving multimodal Large Language Models (LLMs) urgently require new benchmarks to uniformly evaluate their performance on understanding and textually describing music…
MelodyGLM: Multi-task Pre-training for Symbolic Melody Generation
Xinda Wu, Zhijie Huang, Kejun Zhang +5
Pre-trained language models have achieved impressive results in various music understanding and generation tasks. However, existing pre-training methods for symbolic melody generat…