1 citations · 1 across the 11 of their papers we have counts for
13 papers
Collapse, Repair, and Drift: Dissecting Fine Tuning Dynamics in Speech Language Models
Yichi Wang, Wangjin Zhou, Yizhou Zhang +4
Supervised fine tuning (SFT) is commonly monitored through training and evaluation loss, but loss does not fully characterize how model functionality evolves along the same traject…
Direct or Mediated? Task-Dependent Audio Information Routing in Large Audio Language Models
Yizhou Zhang, Wangjin Zhou, Xin Gu +6
Large Audio Language Models (LALMs) have demonstrated strong performance across a wide range of audio understanding tasks. However, they are typically evaluated on single, coherent…
Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation
Yizhou Zhang, Wangjin Zhou, Yi Zhao +3
Music aesthetics scoring plays a critical role in applications such as dataset curation, generative model evaluation, and reward modeling for music generation. Recent approaches re…
LeVo 2: Stable and Melodious Song Generation via Hierarchical Representation Modeling and Progressive Post-Training
Shun Lei, Huaicheng Zhang, Dapeng Wu +8
Full-length song generation must preserve coherence and musicality, render detailed vocal and accompaniment acoustics, and follow lyrics and prompts. Existing language model-based…
SongBench: A Fine-Grained Multi-Aspect Benchmark for Song Quality Assessment
Dapeng Wu, Shun Lei, Wei Tan +5
Recent advancements in Text-to-Song generation have enabled realistic musical content production, yet existing evaluation benchmarks lack the professional granularity to capture mu…
SongPrep: A Preprocessing Framework and End-to-end Model for Full-song Structure Parsing and Lyrics Transcription
Wei Tan, Shun Lei, Huaicheng Zhang +6
Artificial Intelligence Generated Content (AIGC) is currently a popular research area. Among its various branches, song generation has attracted growing interest. Despite the abund…
Towards Hallucination-Free Music: A Reinforcement Learning Preference Optimization Framework for Reliable Song Generation
Huaicheng Zhang, Wei Tan, Guangzheng Li +8
Recent advances in audio-based generative language models have accelerated AI-driven lyric-to-song generation. However, these models frequently suffer from content hallucination, p…
SongBloom: Coherent Song Generation via Interleaved Autoregressive Sketching and Diffusion Refinement
Chenyu Yang, Shuai Wang, Hangting Chen +3
Generating music with coherent structure, harmonious instrumental and vocal elements remains a significant challenge in song generation. Existing language models and diffusion-base…
LeVo: High-Quality Song Generation with Multi-Preference Alignment
Shun Lei, Yaoxun Xu, Zhiwei Lin +10
Recent advances in large language models (LLMs) and audio language models have significantly improved music generation, particularly in lyrics-to-song generation. However, existing…
SongEditor: Adapting Zero-Shot Song Generation Language Model as a Multi-Task Editor
Chenyu Yang, Shuai Wang, Hangting Chen +7
The emergence of novel generative modeling paradigms, particularly audio language models, has significantly advanced the field of song generation. Although state-of-the-art models…
XMusic: Towards a Generalized and Controllable Symbolic Music Generation Framework
Sida Tian, Can Zhang, Wei Yuan +2
In recent years, remarkable advancements in artificial intelligence-generated content (AIGC) have been achieved in the fields of image synthesis and text generation, generating con…
MuQ: Self-Supervised Music Representation Learning with Mel Residual Vector Quantization
Haina Zhu, Yizhi Zhou, Hangting Chen +6
Recent years have witnessed the success of foundation models pre-trained with self-supervised learning (SSL) in various music informatics understanding tasks, including music taggi…
MuCodec: Ultra Low-Bitrate Music Codec
Yaoxun Xu, Hangting Chen, Jianwei Yu +5
Music codecs are a vital aspect of audio codec research, and ultra low-bitrate compression holds significant importance for music transmission and generation. Due to the complexity…