3 citations · 5 across the 5 of their papers we have counts for
4 papers · 1 filter
Elastic Time: Dynamic Frame Rate Bottlenecks for Neural Audio Coding
Dimitrios Bralios, Paris Smaragdis, Minje Kim
Neural audio autoencoders have become a core component of compression, feature extraction, and generation. However, while existing systems support variable bitrate, the vast majori…
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…
Complete and separate: Conditional separation with missing target source attribute completion
Dimitrios Bralios, Efthymios Tzinis, Paris Smaragdis
Recent approaches in source separation leverage semantic information about their input mixtures and constituent sources that when used in conditional separation models can achieve…