7 citations · 12 across the 8 of their papers we have counts for
9 papers
AutoSynth: Learning to Generate Editable Synthesizer Programs from Audio and Text
Tristan Wu, Daniel Chin, Liwei Lin +2
Audio generation models can translate natural-language descriptions into sound, but their outputs are typically waveforms. Their audio quality is constrained by audio compression,…
DDSynth-RL: Audio Synthesizer Inversion via Discrete Diffusion with Reinforcement Learning
Tristan Wu, Daniel Chin, Junan Zhang +3
Synthesizer inversion is challenging for two main reasons: 1) Distinct parameter configurations can produce perceptually similar sounds. 2) Parameter-space losses often fail to ref…
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…
Language Model Mapping in Multimodal Music Learning: A Grand Challenge Proposal
Daniel Chin, Gus Xia
We have seen remarkable success in representation learning and language models (LMs) using deep neural networks. Many studies aim to build the underlying connections among differen…
Human-Centered LLM-Agent User Interface: A Position Paper
Daniel Chin, Yuxuan Wang, Gus Xia
Large Language Model (LLM) -in-the-loop applications have been shown to effectively interpret the human user's commands, make plans, and operate external tools/systems accordingly.…
Learning Interpretable Low-dimensional Representation via Physical Symmetry
Xuanjie Liu, Daniel Chin, Yichen Huang +1
We have recently seen great progress in learning interpretable music representations, ranging from basic factors, such as pitch and timbre, to high-level concepts, such as chord an…