5 citations · 12 across the 8 of their papers we have counts for
8 papers
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…
MM-PoisonRAG: Disrupting Multimodal RAG with Local and Global Poisoning Attacks
Hyeonjeong Ha, Qiusi Zhan, Jeonghwan Kim +6
Retrieval-augmented generation (RAG) has become a common practice in multimodal large language models (MLLM) to enhance factual grounding and reduce hallucination. Yet, its relianc…
Generation or Replication: Auscultating Audio Latent Diffusion Models
Dimitrios Bralios, Gordon Wichern, François G. Germain +4
The introduction of audio latent diffusion models possessing the ability to generate realistic sound clips on demand from a text description has the potential to revolutionize how…
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…
Latent Iterative Refinement for Modular Source Separation
Dimitrios Bralios, Efthymios Tzinis, Gordon Wichern +2
Traditional source separation approaches train deep neural network models end-to-end with all the data available at once by minimizing the empirical risk on the whole training set.…
Unified Gradient Reweighting for Model Biasing with Applications to Source Separation
Efthymios Tzinis, Dimitrios Bralios, Paris Smaragdis
Recent deep learning approaches have shown great improvement in audio source separation tasks. However, the vast majority of such work is focused on improving average separation pe…