Nicholas J. Bryan

🎵🎵What if we could generate video soundtracks without paired video–music data? Introducing V2M-Zero, a method that generates music synchronized with video events. arxiv.org/abs/2603.11042 w. @CasebeerJonah @mtlong_88 @aniruddha26398 @gberta227 @NicholasJBryan

🎵🎵What if we could generate video soundtracks without paired video–music data? Introducing V2M-Zero, a method that generates music synchronized with video events. arxiv.org/abs/2603.11042 w. @CasebeerJonah @mtlong_88 @aniruddha26398 @gberta227 @NicholasJBryan

🎵🎵What if we could generate video soundtracks without paired video–music data? Introducing V2M-Zero, a method that generates music synchronized with video events. arxiv.org/abs/2603.11042 w. @CasebeerJonah @mtlong_88 @aniruddha26398 @gberta227 @NicholasJBryan

GenAE: An audio autoencoder engineered for generative modeling. To appear at ICASSP 2026. w/ @__gzhu__ @zhepeiw03 @NicholasJBryan arXiv: arxiv.org/abs/2602.15749 Video: youtu.be/gDIIuLb0cf0

This is big. SOTA audio reasoning. SOTA video reasoning. SOTA audio captioning. SOTA sound event detection. Better than Gemini. Better than Qwen. TAC: Timestamped Audio Captioning 📑 paper: lnkd.in/getEz5xU 🌐 website with more demos: lnkd.in/gdw5TTuS

Excited to announce our ICASSP 2026 paper "Stemphonic: All-at-once Flexible Multi-stem Music Generation" ! w/ @__gzhu__, @j_p_caceres, @huangcza, and @NicholasJBryan 🔊Demo stemphonic-demo.vercel.app 📰Paper arxiv.org/abs/2602.09891 More details in🧵

Excited to announce our ICASSP 2026 paper "Stemphonic: All-at-once Flexible Multi-stem Music Generation" ! w/ @__gzhu__, @j_p_caceres, @huangcza, and @NicholasJBryan 🔊Demo stemphonic-demo.vercel.app 📰Paper arxiv.org/abs/2602.09891 More details in🧵

Tired to go back to the original papers again and again? Our monograph: a systematic and fundamental recipe you can rely on! 📘 We’re excited to release 《The Principles of Diffusion Models》— with @DrYangSong, @gimdong58085414, @mittu1204, and @StefanoErmon. It traces the core ideas that shaped diffusion modeling and explains how today’s models work, why they work, and where they’re heading. 🧵You’ll find the link and a few highlights in the thread. We’d love to hear your thoughts and join some discussions! ⚡ Stay tuned for our markdown version, where you can drop your comments!

Introducing "DRAGON: Distributional Rewards Optimize Diffusion Generative Models"! 📖: arxiv.org/abs/2504.15217 🎹: ml-dragon.github.io/web/ A new framework for fine-tuning gen models towards a target distribution. By Yatong Bai w/@CasebeerJonah @somayeh_sojoudi @NicholasJBryan

Introducing "DRAGON: Distributional Rewards Optimize Diffusion Generative Models"! 📖: arxiv.org/abs/2504.15217 🎹: ml-dragon.github.io/web/ A new framework for fine-tuning gen models towards a target distribution. By Yatong Bai w/@CasebeerJonah @somayeh_sojoudi @NicholasJBryan