Spatial-Omni: New Method Enables Multimodal AI Models to Understand Spatial Audio
Researchers have proposed Spatial-Omni, a lightweight framework that integrates First-Order Ambisonics (FOA) spatial audio into existing multimodal large language models without altering their original audio encoders. Most current multimodal LLMs process audio as monaural signals, discarding spatial cues needed for sound localization and scene understanding. The work addresses a meaningful gap in AI audio perception, enabling models to reason about the three-dimensional spatial relationships of sounds.
A team of researchers has introduced Spatial-Omni, a method designed to give multimodal large language models (LLMs) the ability to understand spatial audio encoded in First-Order Ambisonics (FOA) format. The core contribution is an SO-Encoder that injects spatial audio as an independent modality, producing spatial tokens with limited additional computational cost and leaving the model's original audio encoder intact. To train and benchmark the system, the authors constructed three resources: SO-Dataset, SO-QA, and SO-Bench, comprising 400,000 FOA audio clips and 2.1 million spatial question-answering pairs drawn from open-source data, real recordings, and simulations. SO-Bench evaluates 16 distinct spatial audio understanding subtasks, ranging from basic sound detection and location estimation to complex spatial relation reasoning. Experiments indicate that Spatial-Omni outperforms existing open-source Large Audio-Language Models and Omni LLMs on spatial audio tasks while maintaining competitive general audio understanding. The code and datasets have been made publicly available, facilitating further research in this area.
What's missing
Comparisons are limited to open-source models; performance relative to closed-source commercial systems (e.g., GPT-4o audio) is not evaluated.
What different sources said
- arXiv cs.AICenter
Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding
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