New audio SSL paper maps objectives to architectures for better downstream tasks

A new arXiv paper from Kele Xu and colleagues at China’s National University of Defense Technology provides a systematic framework for understanding audio self-supervised learning (SSL). Instead of a chronological survey, the authors organize the field around five pretraining paradigms: auxiliary tasks, contrastive learning, generative reconstruction, discrete token prediction, and multimodal alignment. Each objective imposes unique demands on model architecture—local structural sensitivity, contrastive invariance, contextual inference, discrete semantic abstraction, or multimodal grounding.

The paper then maps these demands to the inductive biases of CNNs, recurrent/State Space Models (SSMs), Transformers, and hybrid architectures. For example, CNNs excel at local acoustic compression, while Transformers offer content-dependent global routing. SSMs provide efficient sequential state propagation. The authors evaluate how well each architecture serves downstream tasks like speech processing, environmental sound analysis, music information retrieval, and medical/bioacoustic classification. They also highlight remaining challenges: tokenization bottlenecks in codec-based models, long-context efficiency for streaming audio, robustness to noisy domains, and secure multimodal deployment. A companion repository accompanies the work.