Portrait of Yusong Wu is unavailable

Yusong Wu

PhD - Université de Montréal
Supervisor
Anna (Cheng-Zhi) Huang
Co-supervisor
Aaron Courville
Research Topics
Deep Learning
Generative Models

Publications

Generative Adversarial Post-Training Mitigates Reward Hacking in Live Human-AI Music Interaction
Yusong Wu
Stephen Brade
Aleksandra Teng Ma
Tia-Jane Fowler
Enning Yang
Berker Banar
Aaron Courville
Natasha Jaques
Cheng-Zhi Anna Huang
Most applications of generative AI involve a sequential interaction in which a person inputs a prompt and waits for a response, and where re… (see more)action time and adaptivity are not important factors. In contrast, live jamming is a collaborative interaction that requires real-time coordination and adaptation without access to the other player’s future moves, while preserving diversity to sustain a creative flow. Reinforcement learning post-training enables effective adaptation through on-policy interaction, yet it often reduces output diversity by exploiting coherence-based rewards. This collapse, known as ``reward hacking'', affects many RL post-training pipelines, but is especially harmful in live jamming, where musical creativity relies on dynamic variation and mutual responsiveness. In this paper, we propose a novel adversarial training method on policy-generated trajectories to mitigate reward hacking in RL post-training for melody-to-chord accompaniment. A co-evolving discriminator separates policy trajectories from the data distribution, while the policy maximizes the discriminator output in addition to coherence rewards to prevent collapse to trivial outputs. We evaluate accompaniment quality and output diversity in simulation with both fixed test melodies and learned melody agents, and we conduct a user study with the model deployed in a real-time interactive system with expert musicians. Quantitative evaluation and user feedback demonstrate improved output diversity, harmonic coherence, adaptation speed and user agency. Our results demonstrate a simple yet effective method to mitigate reward hacking in RL post-training of generative sequence models.
2025-12-31
International Conference on Learning Representations (Accept (Poster))
doi.org
openreview.net
FLAM: Frame-Wise Language-Audio Modeling
Yusong Wu
Christos Tsirigotis
Ke Chen
Cheng-Zhi Anna Huang
Aaron Courville
Oriol Nieto
Prem Seetharaman
Justin Salamon
Recent multi-modal audio-language models (ALMs) excel at text-audio retrieval but struggle with frame-wise audio understanding. Prior works … (see more)use temporal-aware labels or unsupervised training to improve frame-wise capabilities, but they still lack fine-grained labeling capability to pinpoint when an event occurs. While traditional sound event detection models can precisely localize events, they are limited to pre-defined categories, making them ineffective for real-world scenarios with out-of-distribution events. In this work, we introduce FLAM, an open-vocabulary contrastive audio-language model capable of localizing specific sound events. FLAM employs a memory-efficient and calibrated frame-wise objective with logit adjustment to address spurious correlations, such as event dependencies and label imbalances during training. To enable frame-wise supervision, we leverage a large-scale dataset with diverse audio events, LLM-generated captions and simulation. Experimental results and case studies demonstrate that FLAM significantly improves the open-vocabulary localization capability while maintaining strong performance in global retrieval and downstream tasks.
2025-04-30
ICML.cc/2025/Conference (poster)
doi.org
proceedings.mlr.press
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
Cheng-Zhi Anna Huang
Jamming requires coordination, anticipation, and collaborative creativity between musicians. Current generative models of music produce expr… (see more)essive output but are not able to generate in an \emph{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.
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
SUNMASK: Mask Enhanced Control in Step Unrolled Denoising Autoencoders
Kyle Kastner
Tim Cooijmans
Yusong Wu
Aaron Courville
2022-12-31
EvoMUSART@EvoStar (published)
doi.org
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
Cheng-Zhi Anna Huang
Jesse Engel
Musical expression requires control of both what notes are played, and how they are performed. Conventional audio synthesizers provide detai… (see more)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.
2022-01-27
ICLR.cc/2022/Conference (oral)
doi.org
openreview.net