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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

• fine-tuning
• benchmarks