2 citations · 2 across the 5 of their papers we have counts for
5 papers · 1 filter
MUUNRiver-Bench: Diagnosing Relation-Dependent Music Retrieval with Multimodal Instructions
Zhancheng Guo, Congren Dai, Shangda Wu +4
Music retrieval is relation-dependent: given a reference track, a listener may seek its style with a new theme, a cover, or a comparable voice, and these intents demand contradicto…
TUTTI: Toward generalizable audio-to-score transcription via fully synthesized data
Jianhuai Hu, Yashan Wang, Shangda Wu +7
Generalizable Audio-to-Score (A2S) transcription is fundamentally constrained by the severe scarcity of high-quality, real-world paired data. Relying solely on existing human-annot…
Shao: Scaling Acoustic Token Language Models Toward High-Fidelity Music Generation
Jiafeng Liu, Yuanliang Dong, Hongjia Liu +8
A common design pattern in high-quality music generation is to handle structure and fidelity in different representation spaces: a generator first models high-level structure, foll…
CLaMP 3: Universal Music Information Retrieval Across Unaligned Modalities and Unseen Languages
Shangda Wu, Zhancheng Guo, Ruibin Yuan +7
CLaMP 3 is a unified framework developed to address challenges of cross-modal and cross-lingual generalization in music information retrieval. Using contrastive learning, it aligns…
CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models
Shangda Wu, Yashan Wang, Ruibin Yuan +12
Challenges in managing linguistic diversity and integrating various musical modalities are faced by current music information retrieval systems. These limitations reduce their effe…