Portrait de Yusong Wu n'est pas disponible

Yusong Wu

Doctorat - UdeM
Superviseur⋅e principal⋅e
Anna (Cheng-Zhi) Huang
Co-supervisor
Aaron Courville
Sujets de recherche
Apprentissage profond
Modèles génératifs

Publications

FLAM: Frame-Wise Language-Audio Modeling
Yusong Wu
Christos Tsirigotis
Ke Chen
Anna (Cheng-Zhi) Huang
Aaron Courville
Oriol Nieto
Prem Seetharaman
Justin Salamon
2025-05-01
ICML.cc/2025/Conference (poster)
openreview.net
openreview.net
Adaptive Accompaniment with ReaLchords
Yusong Wu
Tim Cooijmans
Kyle Kastner
Adam Roberts
Ian Simon
Alexander Scarlatos
Chris Donahue
Cassie Tarakajian
Shayegan Omidshafiei
Aaron Courville
Pablo Samuel Castro
Natasha Jaques
Anna (Cheng-Zhi) Huang
Jamming requires coordination, anticipation, and collaborative creativity between musicians. Current generative models of music produce expr… (voir plus)essive output but are not able to generate in an online manner, meaning simultaneously with other musicians (human or otherwise). We propose ReaLchords, an online generative model for improvising chord accompaniment to user melody. We start with an online model pretrained by maximum likelihood, and use reinforcement learning to finetune the model for online use. The finetuning objective leverages both a novel reward model that provides feedback on both harmonic and temporal coherency between melody and chord, and a divergence term that implements a novel type of distillation from a teacher model that can see the future melody. Through quantitative experiments and listening tests, we demonstrate that the resulting model adapts well to unfamiliar input and produce fitting accompaniment. ReaLchords opens the door to live jamming, as well as simultaneous co-creation in other modalities.
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
proceedings.mlr.press
openreview.net
MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling
Yusong Wu
Ethan Manilow
Yi Deng
Rigel Swavely
Kyle Kastner
Tim Cooijmans
Aaron Courville
Anna (Cheng-Zhi) Huang
Jesse Engel
Musical expression requires control of both what notes are played, and how they are performed. Conventional audio synthesizers provide detai… (voir plus)led expressive controls, but at the cost of realism. Black-box neural audio synthesis and concatenative samplers can produce realistic audio, but have few mechanisms for control. In this work, we introduce MIDI-DDSP a hierarchical model of musical instruments that enables both realistic neural audio synthesis and detailed user control. Starting from interpretable Differentiable Digital Signal Processing (DDSP) synthesis parameters, we infer musical notes and high-level properties of their expressive performance (such as timbre, vibrato, dynamics, and articulation). This creates a 3-level hierarchy (notes, performance, synthesis) that affords individuals the option to intervene at each level, or utilize trained priors (performance given notes, synthesis given performance) for creative assistance. Through quantitative experiments and listening tests, we demonstrate that this hierarchy can reconstruct high-fidelity audio, accurately predict performance attributes for a note sequence, independently manipulate the attributes of a given performance, and as a complete system, generate realistic audio from a novel note sequence. By utilizing an interpretable hierarchy, with multiple levels of granularity, MIDI-DDSP opens the door to assistive tools to empower individuals across a diverse range of musical experience.
2022-01-28
ICLR.cc/2022/Conference (présentation orale)
openreview.net
openreview.net