While language models (LMs) paired with residual vector quantization (RVQ) tokenizers have shown promise in text-to-audio (T2A) generation, they still lag behind diffusion-based models by a non-trivial margin. We identify a critical dilemma underpinning this gap: incorporating more RVQ layers improves audio reconstruction fidelity but exceeds the generation capacity of conventional LMs. To address this, we first analyze RVQ dynamics and uncover two key limitations: 1) orthogonality of features across RVQ layers hinders effective LMs training, and 2) descending semantic richness in tokens from deeper RVQ layers exacerbates exposure bias during autoregressive decoding. Based on these insights, we propose Siren, a novel LM-based framework that employs multiple isolated transformers with causal conditioning and anti-causal alignment via reinforcement learning. Extensive experiments demonstrate that Siren outperforms both existing LM-based and diffusion-based T2A systems, achieving state-of-the-art results. By bridging the representational strengths of LMs with the fidelity demands of audio synthesis, our approach repositions LMs as competitive contenders against diffusion models in T2A tasks. Moreover, by aligning audio representations with linguistic structures, Siren facilitates a promising pathway toward unified multi-modal generation frameworks.
MR-DAW: Towards Collaborative Digital Audio Workstations in Mixed Reality
SolJAMM Research（SolJAMM研究）；University of Colorado Boulder（科罗拉多大学博尔德分校）；ATLAS Insitute（ATLAS研究所）；Hsinchu County Liou-Jia Senior High School（新竹县柳家高中）；National Taiwan Normal University（台湾师范大学）；University of Trento（特伦托大学）
LLM-Based Synthetic Ground Truth Generation for Audio-Based Emotion Classification via In-Context Learning
School of Business, Technical University of Applied Sciences Augsburg（应用技术大学阿沙芬堡商学院）；Data Science und Autonome Systeme Technologietransferzentrum (TTZ)（数据科学与自主系统技术转移中心（TTZ））
