most citedFoundation Models for Music: A Survey

8 citations · 16 across the 8 of their papers we have counts for

collaborators

8 papers

cs.SD2025

Multi-Accent Mandarin Dry-Vocal Singing Dataset: Benchmark for Singing Accent Recognition

Zihao Wang, Ruibin Yuan, Ziqi Geng +7

Singing accent research is underexplored compared to speech accent studies, primarily due to the scarcity of suitable datasets. Existing singing datasets often suffer from detail l…

cs.SD2025★ 2 cited

Singing Timbre Popularity Assessment Based on Multimodal Large Foundation Model

Zihao Wang, Ruibin Yuan, Ziqi Geng +7

Automated singing assessment is crucial for education and entertainment. However, existing systems face two fundamental limitations: reliance on reference tracks, which stifles cre…

cs.SD2025

Automatic Melody Reduction via Shortest Path Finding

Ziyu Wang, Yuxuan Wu, Roger B. Dannenberg +1

Melody reduction, as an abstract representation of musical compositions, serves not only as a tool for music analysis but also as an intermediate representation for structured musi…

eess.AS2025★ 1 cited

YuE: Scaling Open Foundation Models for Long-Form Music Generation

Ruibin Yuan, Hanfeng Lin, Shuyue Guo +55

We tackle the task of long-form music generation--particularly the challenging \textbf{lyrics-to-song} problem--by introducing YuE, a family of open foundation models based on the…

cs.SD2025

Everyone-Can-Sing: Zero-Shot Singing Voice Synthesis and Conversion with Speech Reference

Shuqi Dai, Yunyun Wang, Roger B. Dannenberg +1

We propose a unified framework for Singing Voice Synthesis (SVS) and Conversion (SVC), addressing the limitations of existing approaches in cross-domain SVS/SVC, poor output musica…

cs.SD2024★ 8 cited

Foundation Models for Music: A Survey

Yinghao Ma, Anders Øland, Anton Ragni +39

In recent years, foundation models (FMs) such as large language models (LLMs) and latent diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This com…

cs.SD2024★ 1 cited

Modeling Analog Dynamic Range Compressors using Deep Learning and State-space Models

Hanzhi Yin, Gang Cheng, Christian J. Steinmetz +3

We describe a novel approach for developing realistic digital models of dynamic range compressors for digital audio production by analyzing their analog prototypes. While realistic…

cs.SD2024★ 4 cited

ChatMusician: Understanding and Generating Music Intrinsically with LLM

Ruibin Yuan, Hanfeng Lin, Yi Wang +32

While Large Language Models (LLMs) demonstrate impressive capabilities in text generation, we find that their ability has yet to be generalized to music, humanity's creative langua…