AI Insight
Researchers developed Spatial-Omni, a system that enables multimodal large language models to process spatial audio by incorporating First-Order Ambisonics (FOA) encoding through a lightweight SO-Encoder module. The system was trained and evaluated on SO-Dataset containing 400,000 FOA spatial audio clips and 2.1 million question-answering pairs covering 16 spatial audio understanding tasks including sound localization, spatial relation reasoning, and scene understanding. Spatial-Omni outperformed existing open-source audio-language models on spatial audio tasks while maintaining general audio understanding capabilities.
Why it matters
This advancement could improve AI systems' ability to understand spatial audio environments, with applications in augmented reality, robotics navigation, assistive technologies for the visually impaired, and immersive audio experiences. The lightweight integration approach allows existing multimodal models to gain spatial audio capabilities without complete retraining.
arXiv:2606.10738v1 Announce Type: cross
Abstract: Recent multimodal large language models mainly process audio as monaural signals, thereby discarding the spatial cues contained in spatial audio for sound localization, spatial relation reasoning, and spatial scene understanding. We propose Spatial-Omni, a lightweight method that implements SO-Encoder to inject First-Order Ambisonics (FOA) spatial audio into existing Omni LLMs as an independent modality, without modifying their original audio encoders. SO-Encoder provides spatial tokens with limited additional context cost and improves spatial audio understanding through efficient staged training. To support training and evaluation, we construct SO-Dataset, SO-QA, and SO-Bench from open-source data, real recordings, and simulations, containing 400K FOA spatial audio clips and 2.1M spatial question answering pairs. SO-Bench covers 16 spatial audio understanding subtasks, including basic detection and location estimation, spatial relation understanding, and complex spatial reasoning. Experiments show that Spatial-Omni outperforms existing open-source Large Audio-Language Models (LALMs) and Omni LLM models on spatial audio understanding tasks while retaining a reasonable level of general audio understanding. Code and data are available at https://github.com/dieKarotte/Spatial-Omni.
Source: Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding