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About this paper
Aakanksha Rana, Cagri Ozcinar, Aljoscha Smolic
A research initiative from Trinity College Dublin introduces a novel pipeline that automatically generates Ambisonics (full-sphere spatial audio) for 360° videos by leveraging audio-visual cues from standard recordings. This work includes the creation of a new annotated 360° audio-visual dataset and demonstrates that cubical video projection, combined with a self-supervised deep learning module, achieves superior sound source localization for spatial audio synthesis.
Key insights
- Leveraging audio-visual cues in 360° videos is a viable approach for automatically localizing sound sources, providing a foundation for Ambisonics generation.
- The choice of 360° video representation significantly impacts localization performance, with cubical projection offering advantages over equirectangular due to reduced distortion.
- Adapting deep learning architectures designed for 2D audio-visual tasks can be effective for 3D sound source localization within complex 360° spherical environments.
Summary by alphaXiv.
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