5 citations · 5 across the 5 of their papers we have counts for
4 papers · 1 filter
Downstream-Task-Aware Unified Source Separation
Yoshiki Mitsui, Ryo Aihara, Tatsuhiko Saito +5
Task-aware unified source separation (TUSS) enables a single model to handle diverse separation tasks by conditioning on input prompts. However, conventional TUSS does not account…
SUNAC: Source-aware Unified Neural Audio Codec
Ryo Aihara, Yoshiki Masuyama, Francesco Paissan +3
Neural audio codecs (NACs) provide compact representations that can be leveraged in many downstream applications, in particular large language models. Yet most NACs encode mixtures…
Exploring Disentangled Neural Speech Codecs from Self-Supervised Representations
Ryo Aihara, Yoshiki Masuyama, Gordon Wichern +2
Neural audio codecs (NACs), which use neural networks to generate compact audio representations, have garnered interest for their applicability to many downstream tasks -- especial…
Factorized RVQ-GAN For Disentangled Speech Tokenization
Sameer Khurana, Dominik Klement, Antoine Laurent +13
We propose Hierarchical Audio Codec (HAC), a unified neural speech codec that factorizes its bottleneck into three linguistic levels-acoustic, phonetic, and lexical-within a single…