3 citations · 7 across the 11 of their papers we have counts for
13 papers
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…
Verifier-Guided Twelve-Tone Composition: A Generate-Verify-Repair Harness for Symbolic Music Generation
Congren Dai, Danni Zhao, Enyang Liu +7
Large language models can produce superficially legal twelve-tone scores that collapse into degenerate textures. We introduce a neuro-symbolic harness that wraps a language-model p…
AutoVecCoder: Teaching LLMs to Generate Explicitly Vectorized Code
Shangzhan Li, Xinyu Yin, Xuanyu Jin +8
Vectorization via Single Instruction, Multiple Data (SIMD) architectures is a cornerstone of high-performance computing. To fully exploit hardware potential, developers often resor…
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…
Musical Score Understanding Benchmark: Evaluating Large Language Models' Comprehension of Complete Musical Scores
Congren Dai, Yue Yang, Krinos Li +12
Understanding complete musical scores entails integrated reasoning over pitch, rhythm, harmony, and large-scale structure, yet the ability of Large Language Models and Vision--Lang…
NotaGen: Advancing Musicality in Symbolic Music Generation with Large Language Model Training Paradigms
Yashan Wang, Shangda Wu, Jianhuai Hu +7
We introduce NotaGen, a symbolic music generation model aiming to explore the potential of producing high-quality classical sheet music. Inspired by the success of Large Language M…
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…
Exploring Tokenization Methods for Multitrack Sheet Music Generation
Yashan Wang, Shangda Wu, Xingjian Du +1
This study explores the tokenization of multitrack sheet music in ABC notation, introducing two methods--bar-stream and line-stream patching. We compare these methods against exist…
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…
MelodyT5: A Unified Score-to-Score Transformer for Symbolic Music Processing
Shangda Wu, Yashan Wang, Xiaobing Li +2
In the domain of symbolic music research, the progress of developing scalable systems has been notably hindered by the scarcity of available training data and the demand for models…
Beyond Language Models: Byte Models are Digital World Simulators
Shangda Wu, Xu Tan, Zili Wang +3
Traditional deep learning often overlooks bytes, the basic units of the digital world, where all forms of information and operations are encoded and manipulated in binary format. I…
Generating Chord Progression from Melody with Flexible Harmonic Rhythm and Controllable Harmonic Density
Shangda Wu, Yue Yang, Zhaowen Wang +2
Melody harmonization, which involves generating a chord progression that complements a user-provided melody, continues to pose a significant challenge. A chord progression must not…