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
The rapid advancement of multimodal LLMs necessitates addressing current limitations in audio processing, specifically the lack of spatial understanding, to unlock deeper environmental comprehension.
Integrating spatial audio into LLMs will significantly enhance their ability to interpret and interact with physical environments, moving beyond monaural sound to localized and contextual soundscapes.
LLMs can now process and reason about spatial cues from audio, enabling more sophisticated applications in robotics, virtual reality, and human-computer interaction where spatial context is critical.
- · AI developers
- · Generative AI companies
- · Robotics
- · Virtual reality sector
- · Monosound-centric audio processing techniques
Multimodal LLMs gain a richer understanding of auditory environments.
This leads to more intelligent and context-aware AI agents and embodied AI systems.
The enhanced spatial awareness could accelerate the development of fully autonomous agents capable of navigating and performing complex tasks in dynamic real-world settings.
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Read at arXiv cs.AI