arXiv AI
Bearings: Self-Supervised Soundfield Embeddings from First-Order Ambisonics
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
Hugging Face Trending Papers
Jun 23
MJEPA: A Simple and Scalable Joint-Embedding Predictive Architecture for Audio-Visual Learning
Self-supervised learning from large-scale video data has emerged as a dominant paradigm for visual representation learning. Since audio and visual streams naturally co-occur in video data, extending this success to jointly learn from both modalities is a natural next step, yet it remains challenging.
arXiv AI
Jun 6
F3-Tokenizer: Taming Audio Autoencoder Latents for Understanding and Generation
arXiv:2606. 06357v1 Announce Type: cross Abstract: Continuous audio autoencoders reconstruct waveforms well but often produce latents with weak structure for understanding, while self-supervised audio encoders capture semantics but are not directly decodable.
arXiv AI
Jun 30
Semi-Supervised Sound Event Detection with Conditional Mixup and Embedding-Level Contrastive Loss
arXiv:2606. 29901v1 Announce Type: cross Abstract: Sound event detection (SED) is a core module for acoustic environmental analysis, yet its performance is often limited by scarce labeled data.
arXiv AI
Jun 15
Spatio-Temporal Audio Language Modeling for Dynamic Sound Sources
arXiv:2606. 14141v1 Announce Type: cross Abstract: Sound events are entities with semantic identities, locations, and trajectories, but current audio-language models usually reason about clips as global event content.
arXiv AI
Jul 29
Unlocking Spatial Grounding in Large Audio-Visual Retrieval models
arXiv:2607. 24786v1 Announce Type: cross Abstract: Weak supervision sets a practical regime for audio-visual sound source localization as dense spatial annotations are costly to obtain at scale.
arXiv Machine Learning
Jun 25
MJEPA: A Simple and Scalable Joint-Embedding Predictive Architecture for Audio-Visual Learning
arXiv:2606. 25225v1 Announce Type: cross Abstract: Self-supervised learning from large-scale video data has emerged as a dominant paradigm for visual representation learning.