Bearings: Self-Supervised Soundfield Embeddings from First-Order Ambisonics
Recently proposed self-supervised audio encoders learn powerful general-purpose representations of sound scenes, yet they are spatially blind. To supply the missing spatial representation of sound scenes, we introduce Bearings. Bearings is a self-supervised framework that learns soundfield embeddings from unlabeled first-order Ambisonics. We pre-train a masked auto-encoder paired with a decoder conditioned on frozen acoustic embeddings from an off-the-shelf single-channel audio encoder. Our results show that the resulting soundfield embeddings form a reusable stream that can be attached to frozen acoustic encoders with a lightweight trainable fusion head. On sound event localization and d...
| Authors | Goksenin Yuksel, Marcel van Gerven, Kiki van der Heijden |
|---|---|
| Primary category | cs.SD |
| Published | 19 September 2026 |
| Last updated at source | 19 September 2026 |
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