14 citations · 29 across the 16 of their papers we have counts for
3 papers · 2 filters
Re-Bottleneck: Latent Re-Structuring for Neural Audio Autoencoders
Dimitrios Bralios, Jonah Casebeer, Paris Smaragdis
Neural audio codecs and autoencoders have emerged as versatile models for audio compression, transmission, feature-extraction, and latent-space generation. However, a key limitatio…
Learning to Upsample and Upmix Audio in the Latent Domain
Dimitrios Bralios, Paris Smaragdis, Jonah Casebeer
Neural audio autoencoders create compact latent representations that preserve perceptually important information, serving as the foundation for both modern audio compression system…
DRAGON: Distributional Rewards Optimize Diffusion Generative Models
Yatong Bai, Jonah Casebeer, Somayeh Sojoudi +1
We present Distributional RewArds for Generative OptimizatioN (DRAGON), a versatile framework for fine-tuning media generation models towards a desired outcome. Compared with tradi…