Review
Jul 2026
From Objectives to Applications: Aligning Architectural Biases in Audio Self-Supervised Learning
This paper examines audio self-supervised learning through the alignment between pretraining objectives, architectural inductive biases, and downstream applications, and relates these demands to the biases of CNNs, recurrent and State Space Models, Transformers, and hybrid architectures.
Kele Xu, Yu Fang, Boda Zhou et al.
· 0 citations