activity
20242026
most citedMusic and Artificial Intelligence: Artistic Trends

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.SD2026

Low-Resource Guidance for Controllable Latent Audio Diffusion

Zachary Novack, Zack Zukowski, CJ Carr +6

Generative audio requires fine-grained controllable outputs, yet most existing methods require model retraining on specific controls or inference-time controls (\textit{e.g.}, guid…

cs.CV2025

Foley Control: Aligning a Frozen Latent Text-to-Audio Model to Video

Ciara Rowles, Varun Jampani, Simon Donné +3

Foley Control is a lightweight approach to video-guided Foley that keeps pretrained single-modality models frozen and learns only a small cross-attention bridge between them. We co…

cs.CY20251 cited

Music and Artificial Intelligence: Artistic Trends

Jordi Pons, Zack Zukowski, Julian D. Parker +3

We study how musicians use artificial intelligence (AI) across formats like singles, albums, performances, installations, voices, ballets, operas, or soundtracks. We collect 337 mu…

cs.SD2025

Fast Text-to-Audio Generation with Adversarial Post-Training

Zachary Novack, Zach Evans, Zack Zukowski +8

Text-to-audio systems, while increasingly performant, are slow at inference time, thus making their latency unpractical for many creative applications. We present Adversarial Relat…

eess.AS2024

Scaling Transformers for Low-Bitrate High-Quality Speech Coding

Julian D Parker, Anton Smirnov, Jordi Pons +4

The tokenization of speech with neural audio codec models is a vital part of modern AI pipelines for the generation or understanding of speech, alone or in a multimodal context. Tr…