Hugging Face Trending Papers
AudioFM: Towards General Purpose Audio Representation Learning
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv Machine Learning
Oct 28
AudioFM: Towards General Purpose Audio Representation Learning
AudioFM is a self‑supervised framework that learns audio representations by combining recording‑level contrastive learning with patch‑wise masked latent discrimination. The model uses visible acoustic context for a student to predict masked patch representations and matches them to full‑view teacher targets from other recordings, while also incorporating weaker supervision from temporal neighbors to promote local consistency. Evaluations show that AudioFM achieves state‑of‑the‑art results across diverse audio domains in linear probing, fine‑tuning, and zero‑shot transfer, excelling in event detection, scene‑level understanding, and domain generalizability.
arXiv AI
Aug 21
Listening Forward: Next Patch Embedding Prediction Enables Scalable Audio Learners
arXiv:2608. 19863v1 Announce Type: cross Abstract: Self-supervised learning (SSL) has driven substantial progress in audio representation learning, though existing methods have increasingly relied on elaborate pre-training recipes to reach competitive performance.
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 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.
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
Jul 1
BEST-RQ-2: Contextualize-Then-Predict, a Two-Step Approach for Self-Supervised Audio Representations
arXiv:2606. 30700v1 Announce Type: cross Abstract: Self-supervised learning enables audio representations that transfer across domains and tasks.