Title: The World is Not Mono: Enabling Spatial Understanding in Large Audio-Language Models
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Spatial Audio, Large Audio-Language Models, Acoustic Scene Analysis, Mixture-of-Experts, Reinforcement Learning

Abstract: Existing large audio-language models perceive the world as "mono"—a single stream of audio that ignores the critical spatial dimension ("where") required for universal acoustic scene analysis. To break down this fundamental limitation, we introduce a framework that enables models like Qwen2-Audio to understand and reason about the complex, three-dimensional acoustic world. Our framework achieves this through three core contributions: First, we build a large-scale, synthesized binaural audio dataset to provide the rich spatial cues. Second, we design a novel Mixture-of-Experts (MoE) architecture, where a learnable router directs outputs from parallel semantic and spatial encoders to specialized expert pathways. Finally, we employ a progressive training curriculum, advancing from supervised fine-tuning (SFT) to reinforcement learning via Group Relative Policy Optimization (GRPO), to evolve the model's capabilities from basic perception to advanced reasoning. On our comprehensive benchmark, the model demonstrates a strong capability for spatial understanding. By enabling this spatial perception, our work provides a clear pathway for leveraging the powerful reasoning abilities of large models towards holistic acoustic scene analysis, advancing from one-dimensional semantic recognition to three-dimensional spatial intelligence.

TL;DR: Spatial Understanding in Large Audio-Language Models