9 citations · 18 across the 7 of their papers we have counts for
7 papers
Robust Source-Free Domain Adaptation for Medical Image Segmentation based on Curriculum Learning
Ziqi Zhang, Yuexiang Li, Yawen Huang +6
Recent studies have uncovered a new research line, namely source-free domain adaptation, which adapts a model to target domains without using the source data. Such a setting can ad…
Versatile Symbolic Music-for-Music Modeling via Function Alignment
Junyan Jiang, Daniel Chin, Liwei Lin +2
Many music AI models learn a map between music content and human-defined labels. However, many annotations, such as chords, can be naturally expressed within the music modality its…
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…
Instruct-MusicGen: Unlocking Text-to-Music Editing for Music Language Models via Instruction Tuning
Yixiao Zhang, Yukara Ikemiya, Woosung Choi +7
Recent advances in text-to-music editing, which employ text queries to modify music (e.g.\ by changing its style or adjusting instrumental components), present unique challenges an…
Arrange, Inpaint, and Refine: Steerable Long-term Music Audio Generation and Editing via Content-based Controls
Liwei Lin, Gus Xia, Yixiao Zhang +1
Controllable music generation plays a vital role in human-AI music co-creation. While Large Language Models (LLMs) have shown promise in generating high-quality music, their focus…
Content-based Controls For Music Large Language Modeling
Liwei Lin, Gus Xia, Junyan Jiang +1
Recent years have witnessed a rapid growth of large-scale language models in the domain of music audio. Such models enable end-to-end generation of higher-quality music, and some a…
A Unified Model for Zero-shot Music Source Separation, Transcription and Synthesis
Liwei Lin, Qiuqiang Kong, Junyan Jiang +1
We propose a unified model for three inter-related tasks: 1) to \textit{separate} individual sound sources from a mixed music audio, 2) to \textit{transcribe} each sound source to…