Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding

Researchers introduce Spatial-Omni, a method that integrates First-Order Ambisonics (FOA) spatial audio into multimodal large language models, enabling them to understand sound localization and spatial scene reasoning. The approach includes new datasets and benchmarks with 400K audio clips and 2.1M QA pairs, demonstrating improved performance on spatial audio tasks while maintaining general audio understanding.

Spatial-Omni addresses a significant gap in current multimodal AI systems: the inability to process spatial audio information. Existing large language models treat audio as monaural signals, losing critical directional and positional data that humans naturally use to understand environments. This research introduces an SO-Encoder that efficiently integrates First-Order Ambisonics without modifying underlying architectures, representing a practical engineering approach to expanding model capabilities.

The work reflects broader trends in AI development toward more comprehensive multimodal understanding. As language models expand beyond text and images into audio, capturing full audio dimensionality becomes increasingly important for applications like spatial scene understanding, robotics, and immersive computing. The creation of SO-Dataset, SO-QA, and SO-Bench provides essential infrastructure for evaluating spatial audio capabilities, addressing the persistent challenge of benchmark scarcity in emerging research areas.

The technical contribution matters for developers building audio-centric applications in robotics, autonomous systems, and spatial computing platforms. The lightweight nature of SO-Encoder suggests practical deployment potential without substantial computational overhead. Open-source release of code and datasets accelerates community adoption and further research.

This advance signals growing sophistication in audio processing within AI systems. Future development likely involves integration of spatial audio into larger foundation models and refinement of spatial reasoning capabilities. Researchers and companies working on audio-language systems should monitor continued improvements in this space, as spatial understanding becomes increasingly valuable for real-world applications requiring environmental awareness.

• →Spatial-Omni enables multimodal LLMs to process First-Order Ambisonics spatial audio as an independent modality without architecture modifications.
• →New benchmark datasets include 400K spatial audio clips and 2.1M QA pairs covering 16 subtasks from basic localization to complex reasoning.
• →Method achieves superior performance on spatial audio tasks while maintaining general audio understanding capabilities compared to existing LALMs.
• →Lightweight SO-Encoder design provides spatial tokens with minimal additional computational context cost.
• →Open-source release of code and datasets supports broader adoption and research advancement in spatial audio understanding.