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20212026
most citedFoundation Models for Music: A Survey

8 citations · 32 across the 20 of their papers we have counts for

collaborators

22 papers

eess.AS2026

YuE2: Unifying Symbolic and Audio Music Generation at Frontier Quality

Ruibin Yuan, Jiahao Pan, Junyan Jiang +32

Symbolic models make melody, harmony, rhythm, and form explicit but typically stop before a finished recording; audio models produce complete songs while leaving composition implic…

cs.SD2026

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…

cs.SD2026

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…

cs.SD2026

UniVerse: Benchmarking and Enhancing LALMs on Culturally Inclusive Low-Resource Music Understanding

Ziya Zhou, Shangda Wu, Shenyang Xu +16

Recent advances in large audio-language models (LALMs) have significantly improved performance in tasks such as music captioning, genre classification, and sound event detection. H…

cs.SD2026

Voices of Civilizations: A Multilingual QA Benchmark for Global Music Understanding

Shangda Wu, Ziya Zhou, Yongyi Zang +4

We introduce Voices of Civilizations, the first multilingual QA benchmark for evaluating audio LLMs' cultural comprehension on full-length music recordings. Covering 380 tracks acr…

cs.SD2025★ 1 cited

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…

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

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…

cs.SD2024

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…

cs.SD2024★ 2 cited

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…

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

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…

cs.SD2024★ 3 cited

MuPT: A Generative Symbolic Music Pretrained Transformer

Xingwei Qu, Yuelin Bai, Yinghao Ma +25

In this paper, we explore the application of Large Language Models (LLMs) to the pre-training of music. While the prevalent use of MIDI in music modeling is well-established, our f…

cs.LG2024★ 3 cited

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…

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…

cs.SD2023★ 1 cited

A Holistic Evaluation of Piano Sound Quality

Monan Zhou, Shangda Wu, Shaohua Ji +2

This paper aims to develop a holistic evaluation method for piano sound quality to assist in purchasing decisions. Unlike previous studies that focused on the effect of piano perfo…

cs.IR2023

EMelodyGen: Emotion-Conditioned Melody Generation in ABC Notation with the Musical Feature Template

Monan Zhou, Xiaobing Li, Feng Yu +1

The EMelodyGen system focuses on emotional melody generation in ABC notation controlled by the musical feature template. Owing to the scarcity of well-structured and emotionally la…

cs.SD2023★ 2 cited

CLaMP: Contrastive Language-Music Pre-training for Cross-Modal Symbolic Music Information Retrieval

Shangda Wu, Dingyao Yu, Xu Tan +1

We introduce CLaMP: Contrastive Language-Music Pre-training, which learns cross-modal representations between natural language and symbolic music using a music encoder and a text e…

cs.SD2023★ 1 cited

TunesFormer: Forming Irish Tunes with Control Codes by Bar Patching

Shangda Wu, Xiaobing Li, Feng Yu +1

This paper introduces TunesFormer, an efficient Transformer-based dual-decoder model specifically designed for the generation of melodies that adhere to user-defined musical forms.…

cs.SD2022★ 3 cited

Exploring the Efficacy of Pre-trained Checkpoints in Text-to-Music Generation Task

Shangda Wu, Maosong Sun

Benefiting from large-scale datasets and pre-trained models, the field of generative models has recently gained significant momentum. However, most datasets for symbolic music are…

cs.CL2022★ 2 cited

Efficient and Training-Free Control of Language Generation

Shangda Wu, Maosong Sun

In recent years, there has been a growing interest in the development of language models capable of generating text with controllable attributes. While several approaches have been…

cs.SD2021

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…