UniAE-MoE统一音频编码器发布
UniAE-MoE: A Unified Audio Encoder via Mixture of Experts
UniAE-MoE整合了两种主流音频编码器，通过任务特定数据扩展技术提升跨音频领域理解能力。
研究人员推出UniAE-MoE，一种基于专家混合架构的统一音频编码器。该模型整合了Qwen2-Audio和Audio-Flamingo 3的编码器，在XARES-LLM基准测试中得分为0.802，达到最先进性能。UniAE-MoE在Interspeech 2026音频编码器能力挑战赛中同样表现出色。
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 downstream tasks. Moreover, we propose the task-specific data scaling (TSDS) technique to enhance \tool's understanding capabilities. On the XARES-LLM benchmark, UniAE-MoE attains a score of 0.802, achieving state-of-the-art performance. It also delivers top-tier performance in the official Interspeech 2026 Audio Encoder Capability Challenge, further demonstrating robust generalization across diverse audio tasks. Together, these results validate the effectiveness of \tool for unified audio understanding across speech, music, and general audio domains.
