As a widely used medium in the cyberspace， digital audio serves as an excellent cover for carrying secret information and is often employed in the construction of covert communication systems that prioritize real-time performance， low complexity， and imperceptibility. Audio steganography， one of the key techniques for ensuring network information security and confidential communication， has attracted increasing attention from scholars. This paper presents a systematic review of the development context of audio steganography methods. Firstly， we introduce the basic contents of audio steganography， and summarize the problem description， evaluation indicators， common data formats， and tools. Secondly， according to different embedding domains， traditional audio steganography methods are classified into time domain methods， transform domain methods and compression domain methods， and their advantages and disadvantages are analyzed. Furthermore， based on different steganographic covers， the deep learning-based steganography methods are categorized into embedding cover-based， generating cover-based， and coverless audio steganography， then the three steganography methods are compared and analyzed. Finally， suggestions for further research directions in audio steganography are pointed out.