Similar to humans' cognitive ability to generalize knowledge and skills, self-supervised learning (SSL) targets discovering general representations from large-scale data. This, through the use of pre-trained SSL models for downstream tasks, alleviates the need for human annotation, which is an expensive and time-consuming task. Its success in the fields of computer vision and natural language processing have prompted its recent adoption into the field of audio and speech processing. Comprehensive reviews summarizing the knowledge in audio SSL are currently missing. To fill this gap, we provide an overview of the SSL methods used for audio and speech processing applications. Herein, we also summarize the empirical works that exploit audio modality in multi-modal SSL frameworks and the existing suitable benchmarks to evaluate the power of SSL in the computer audition domain. Finally, we discuss some open problems and point out the future directions in the development of audio SSL.

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