aiqbench analysis
Spatial-Omni introduces a lightweight method to inject First-Order Ambisonics spatial audio into existing multimodal LLMs without altering their original encoders, addressing the current limitation of processing audio as monaural signals. By leveraging a new dataset of 400K spatial audio clips and 2.1M QA pairs, the approach demonstrates improved performance on spatial reasoning tasks while maintaining general audio comprehension. This work offers a practical pathway for integrating spatial cues into large audio-language models with minimal architectural changes.
Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding
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.
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 this https URL.
Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI)
arXiv:2606.10738 [eess.AS]
(or arXiv:2606.10738v1 [eess.AS] for this version)
https://doi.org/10.48550/arXiv.2606.10738 arXiv-issued DOI via DataCite (pending registration)
Full text under the publisher's open licence · 189 words
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