UniAE-MoE: A Unified Audio Encoder via Mixture of Experts
Large Audio Language Models (LALMs) rely on effective audio encoders for multi-task performance. We introduce UniAE-MoE, a unified audio encoder designed to model cross-domain audio representations and achieve outstanding downstream understanding performance via a Mixture-of-Experts (MoE) architecture. Specifically, we explore mainstream audio encoders and integrate those from Qwen2-Audio and Audio-Flamingo 3, which demonstrate superior downstream capabilities. To facilitate effective model fusion, we improve our encoder using SwiGLU with shared experts to decouple encoder networks, and we further introduce a two-stage instruction-tuning strategy to better adapt the model to diverse downs...
| Authors | Shengbo Cai, Zhisheng Zhang, Zichao Nie et al. |
|---|---|
| Primary category | cs.SD |
| Published | 30 September 2026 |
| Last updated at source | 5 October 2026 |
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