Hugging Face Trending Papers
Learning Array Signal Topologies as Conditional Neural Manifolds
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arXiv Machine Learning
Sep 17
Learning Array Signal Topologies as Conditional Neural Manifolds
The paper introduces the Conditional Neural Manifold (CNM), a data‑driven replacement for the fixed array manifold used in subspace methods like MUSIC. CNM learns a mapping from source parameters to steering vectors conditioned on observed snapshots, using an encoder to produce a latent scene representation that drives a neural field over the parameter space. By shaping the resulting MUSIC landscape, CNM restores super‑resolution performance under array imperfections, colored noise, correlated sources, near‑field propagation, and resolves the angle‑frequency ambiguity without requiring steering‑vector supervision.
arXiv AI
Jun 18
NeuralMUSIC: A Hybrid Neural-Subspace Framework for Robot Sound Source Localization
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.
Hugging Face Trending Papers
Jun 17
NeuralMUSIC: A Hybrid Neural-Subspace Framework for Robot Sound Source Localization
Reliable sound source localization is fundamental to robot audition, enabling autonomous robots to perceive spatial cues and operate effectively in dynamic environments. Classical methods such as Multiple Signal Classification (MUSIC) offer strong theoretical foundations but degrade under low signal-to-noise ratios.
arXiv Machine Learning
Jul 7
Weakly Guided and Autoregressive Beamformer Parameterization for Generalizable Moving Speaker Extraction in Higher-Order Ambisonics
arXiv:2607. 04471v1 Announce Type: cross Abstract: Linear spatial filters (beamformers) enable robust, generalizable and interpretable speech enhancement with performance guarantees under ideal parameterization.
arXiv AI
Sep 23
Bearings: Self-Supervised Soundfield Embeddings from First-Order Ambisonics
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...
arXiv Computer Vision
Sep 7
Learning Spatial-Spectral Refinement and Calibrating Complementary Observations for Hyperspectral Image Super-Resolution
The paper introduces TSR-ITNR, a two‑stage, self‑supervised framework for hyperspectral image super‑resolution that fuses high‑resolution multispectral and low‑resolution hyperspectral data. Stage 1 refines an implicit Tucker representation using a low‑rank spatial tensor and spectral basis, enhanced by a pretrained denoiser, to capture fine spatial details and spectral correlations. Stage 2 applies parameter‑free calibration to extract complementary corrections from both observations, preserving geometry and ensuring orthogonal complementarity, leading to superior reconstruction quality demonstrated on benchmark datasets and improved downstream segmentation performance.