arXiv Artificial Intelligence

Supervising Sound Localization by In-the-wild Egomotion

1 hour ago 02.10.2026 07:00 👁 1 views
Supervising Sound Localization by In-the-wild Egomotion

Quick summary

arXiv:2610.01388v1 Announce Type: cross Abstract: We present a method for learning binaural sound localization using egomotion as a supervisory signal. Over the course of a video, the cameras direction to a sound source will change as the camera moves. We train an audio model to predict sound directions that are consistent with visual estimates of camera motion, which we obtain using traditional methods from multi-view geometry. This provides a weak but plentiful form of supervision that we combine with traditional binaural cues. To evaluate this method, we propose a dataset of real-world audi

Key takeaways

  • arXiv:2610.01388v1 Announce Type: cross Abstract: We present a method for learning binaural sound localization using egomotion as a supervisory signal.
  • Over the course of a video, the cameras direction to a sound source will change as the camera moves.
  • We train an audio model to predict sound directions that are consistent with visual estimates of camera motion, which we obtain using traditional methods from multi-view geometry.

Why it matters

“Supervising Sound Localization by In-the-wild Egomotion” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗