SAVU-Bench is a real‑world benchmark designed to evaluate spatial audio‑visual understanding across three capability levels and seven tasks, addressing limitations of prior simulated‑scene datasets. It includes a diagnostic set, SAVU‑Diag, that breaks down reasoning questions into grounding and alignment sub‑tasks, revealing that many errors stem from failures in these prerequisites. A training‑free baseline, SAVU‑EA, improves spatial grounding and joint matching but high‑level reasoning remains difficult, underscoring the need for better audio perception and cross‑modal spatial integration.
By Yu Chen, Ruihang Liu, Yangguang Xu, Xinyue Jiang, Mohammed Bennamoun, Farid Boussaid, Xinyuan Qian, Qiuhong Ke
arXiv:2402. 01591v4 Announce Type: replace-cross Abstract: Spatial sound reasoning is a fundamental human skill, enabling us to navigate and interpret our surroundings based on sound.
By Zhisheng Zheng, Puyuan Peng, Ziyang Ma, Xie Chen, Eunsol Choi, David Harwath
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.
By Zhiyuan Zhu, Yixuan Chen, Yiwen Shao, Wenxiang Guo, Changhao Pan, Yu Zhang, Yuxiang Wang, Wei Liu, Houhua Zhang, Chengkuan Zeng, Wenbo Cheng, Yunxi Liu, Rui Yang, Steve Yves, Liefeng Bo, Zhou Zhao
arXiv:2609.23407v2 Announce Type: replace-cross
Abstract: Humans can effortlessly localize the direction of a sound source and integrate it with visual cues for reasoning, yet this remains challengin...
By Ruixun Liu, Yuxuan Wang, Jiacheng Xie, Yuhuan You, Donghua Cai, Junming Lin, Xiong-Hui Chen, Zhifang Guo, Yunfei Chu, Qize Yang, Xize Cheng, Jin Xu, Yiwu Zhong
FloorSAV is a framework that enhances audio‑visual large language models (AV‑LLMs) by embedding a dynamic 2D floormap derived from 3D point clouds, camera trajectories, spatial audio cues, and object landmarks. The floormap is fed into the AV‑LLM as a synchronized stream with egocentric video, enabling the model to jointly reason over visual, auditory, and geometric information in a single inference. The authors also introduce SAVED‑Bench, a benchmark for spatial reasoning tasks, and show that FloorSAV improves AV‑LLM performance on both SAVED‑Bench and SAVVY‑Bench, with ground‑truth floormaps highlighting its potential.
By Kyeong-Rae Kim, Sungnyun Kim, Tae-Hyun Oh
arXiv:2609.23407v1 Announce Type: cross
Abstract: Humans can effortlessly localize the direction of a sound source and integrate it with visual cues for reasoning, yet this remains challenging for em...
By Ruixun Liu, Yuxuan Wang, Jiacheng Xie, Yuhuan You, Donghua Cai, Junming Lin, Xiong-Hui Chen, Zhifang Guo, Yunfei Chu, Qize Yang, Xize Cheng, Jin Xu, Yiwu Zhong
arXiv:2608. 09435v1 Announce Type: new Abstract: Understanding dynamic sound sources requires jointly determining what produces a sound, where the source is located, and how it moves over time.
By Zhi Zeng, Cheng Zhang, Zesheng Yang, Rendong Pi, Jiaying Wu, Di Zhang, Zihan Ma, Guodong Li, Zhou Yang, Yu Xiang, Yifei Zheng, Minnan Luo
Understanding dynamic sound sources requires jointly determining what produces a sound, where the source is located, and how it moves over time. Yet existing audio-language models often represent clips as global acoustic events, while vision-language models lack the spatial audio cues needed to localize and track individual sources.
Recent Large Audio Language Models (LALMs) have achieved remarkable progress in audio perceptual tasks across individual acoustic layers, including speech, sound, and music. However, existing benchmarks predominantly evaluate these layers in isolation, overlooking the complex contextual relationships that arise when multiple acoustic sources co-occur in real-world auditory scenes.
arXiv:2610.17131v2 Announce Type: cross
Abstract: Dynamic spatial audio reasoning requires a model to associate changing acoustic observations with the correct source and recover its spatial state at...
By Chenlong Wei, Hanling Wang, Mingjin Lou, Yufan Shi, Yandong Sun, Sheldon Yu, Xiaohui Zhu, Xiaobo Jin, Yiwei Wang
arXiv:2607. 24786v1 Announce Type: cross Abstract: Weak supervision sets a practical regime for audio-visual sound source localization as dense spatial annotations are costly to obtain at scale.
By Hugo Malard, Michel Olvera, Sanjeel Parekh, Ga\"el Richard, Slim Essid, St\'ephane Lathuili\`ere
MV-STRIDE is a Multi‑View hierarchical Spatial Reasoning dataset that models dependencies among perception, scene understanding, and contextual reasoning to support 3D spatial cognition. It introduces a QA generation pipeline that enforces cross‑view constraints, producing multi‑level reasoning tasks with chain‑of‑thought supervision. Experiments show that training on MV‑STRIDE yields state‑of‑the‑art performance on multi‑view spatial benchmarks, enabling MLLMs to reason robustly across diverse viewpoints.
By Jin Xu, Xiaojian Huang, Zhuodong Luo, Zhihong Zhang, Xin Liu, Jiansheng Wei, Xinzhi Wang, Jie Zhao, Xuejin Chen