Spatial-Omni: New Method Enables Multimodal AI Models to Understand Spatial Audio
Researchers have developed Spatial-Omni, a method that allows large language models to process spatial audio information using First-Order Ambisonics (FOA) encoding, enabling sound localization and spatial scene understanding. Current multimodal AI models typically treat audio as monaural signals, losing spatial cues that are important for understanding 3D sound environments. The advancement could improve AI applications requiring spatial audio understanding, such as scene analysis and spatial reasoning tasks.
Spatial-Omni is a lightweight technique that integrates spatial audio processing into existing multimodal large language models without requiring modifications to their original audio encoders. The method uses an SO-Encoder component to convert First-Order Ambisonics spatial audio into spatial tokens, adding minimal computational overhead. To validate the approach, researchers created three new datasets: SO-Dataset (400K FOA spatial audio clips), SO-QA (2.1M spatial question-answering pairs), and SO-Bench (covering 16 spatial audio understanding subtasks including detection, localization, spatial relations, and complex reasoning). Experimental results demonstrate that Spatial-Omni outperforms existing open-source large audio-language models and multimodal models on spatial audio tasks while maintaining general audio understanding capabilities. The researchers have made code and data publicly available to support further research.
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- arXiv cs.AICenter
Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding
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