Audio representation learning has revolutionized acoustic intelligence, enabling audio foundation models to generalize and perform well across various downstream tasks. To better characterize the paradigm shift in general representation extraction and modeling, this survey presents a unified decoupled framework separating audio processing into audio-to-feature representation learning and feature-to-target downstream modeling. We systematically review modeling domains, representation forms, architectures, and pre-training paradigms, while categorizing five integration mechanisms and downstream modeling paradigms. Furthermore, we summarize representative systems and discuss key trade-offs, providing practical guidelines for designing next-generation audio foundation models.