collaborators

5 papers

eess.AS2026

VIOLET: High-Fidelity Violin Synthesis with Techniques and Dynamics

Baotong Tian, Cynthia Lu, Vincent K. M. Cheung +3

Neural synthesis for musical instruments has the potential to revolutionize current practices that use concatenative synthesis and a sample library. However, most research focused…

cs.SD2026

VioPTT: Violin Technique-Aware Transcription from Synthetic Data Augmentation

Ting-Kang Wang, Yueh-Po Peng, Li Su +1

While automatic music transcription is well-established in music information retrieval, most models are limited to transcribing pitch and timing information from audio, and thus om…

cs.SD2025

Is Transfer Learning Necessary for Violin Transcription?

Yueh-Po Peng, Ting-Kang Wang, Li Su +1

Automatic music transcription (AMT) has achieved remarkable progress for instruments such as the piano, largely due to the availability of large-scale, high-quality datasets. In co…

eess.IV2025

Whole-brain Transferable Representations from Large-Scale fMRI Data Improve Task-Evoked Brain Activity Decoding

Yueh-Po Peng, Vincent K. M. Cheung, Li Su

A fundamental challenge in neuroscience is to decode mental states from brain activity. While functional magnetic resonance imaging (fMRI) offers a non-invasive approach to capture…

cs.CV2024

Imagine yourself: Tuning-Free Personalized Image Generation

Zecheng He, Bo Sun, Felix Juefei-Xu +14

Diffusion models have demonstrated remarkable efficacy across various image-to-image tasks. In this research, we introduce Imagine yourself, a state-of-the-art model designed for p…