arXiv Machine Learning By Luis Vitor Zerkowski, Luiz Velho

BinauralVAE: Spatial Audio Reconstruction For World Models

Read the original on arXiv Machine Learning →

BinauralVAE is an open‑source pipeline that reconstructs spatial audio using various Variational Autoencoder architectures, including complex‑valued variants, to learn latent representations of binaural signals. The project builds on realistic acoustic data from a simulated robot navigating an environment, providing a foundation for audio‑centric world models. It aims to map the causal link between navigational actions and their acoustic outcomes, positioning sound as a complementary modality for spatial awareness.

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arXiv AI
Jun 10

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.

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 AI
2d ago

CTAN: Cycle-Temporal Attention Network for Embodied Audio-Visual Navigation

The paper introduces CTAN, a Cycle-Temporal Attention Network for audio‑visual embodied navigation. It proposes an Audio‑Visual Reconstruction Cross‑Attention module that uses bidirectional cycle‑consistency to strengthen spatial semantics across visual and acoustic modalities, and a Temporal Cross‑Modal Memory to fuse real‑time multimodal features with historical context. Experiments on Replica and Matterport3D show that CTAN outperforms prior methods in success rate, SPL, and scene navigation accuracy.

By Teng Liu, Yinfeng Yu