arXiv:2606. 18664v1 Announce Type: cross Abstract: Reliable sound source localization is fundamental to robot audition, enabling autonomous robots to perceive spatial cues and operate effectively in dynamic environments.
By Yizhuo Yang, Junqiao Fan, Shenghai Yuan, Lihua Xie
arXiv:2609.23152v1 Announce Type: cross
Abstract: Recently proposed self-supervised audio encoders learn powerful general-purpose representations of sound scenes, yet they are spatially blind. To sup...
By Goksenin Yuksel, Marcel van Gerven, Kiki van der Heijden
The paper introduces a method for learning binaural sound localization by using egomotion as a supervisory signal. By tracking how a camera’s direction changes relative to a sound source during a video, the authors train an audio model to predict sound directions that align with visual estimates of camera motion derived from multi‑view geometry. They evaluate this approach on a newly proposed dataset of real‑world audio‑visual videos with egomotion, demonstrating that the model can learn from real data and perform well on sound localization tasks.
By Anna Min, Ziyang Chen, Hang Zhao, Andrew Owens
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.
By Luis Vitor Zerkowski, Luiz Velho
Humans can selectively attend to a target sound and estimate its direction in complex scenarios, whereas such selective localization remains challenging for current deep learning-based systems. Sound source localization (SSL) has achieved remarkable success with deep learning, yet most methods localize all active sources without selectivity.
arXiv:2601. 21124v2 Announce Type: replace-cross Abstract: Current multimodal LLMs process audio as a mono stream, ignoring the rich spatial information essential for embodied AI.
By Artem Dementyev, Wazeer Zulfikar, Sinan Hersek, Pascal Getreuer, Anurag Kumar, Vivek Kumar