Automated audio captioning (AAC), a task that mimics human perception as well as innovatively links audio processing and natural language processing, has overseen much progress over the last few years. AAC requires recognizing contents such as the environment, sound events and the temporal relationships between sound events and describing these elements with a fluent sentence. Currently, an encoder-decoder-based deep learning framework is the standard approach to tackle this problem. Plenty of works have proposed novel network architectures and training schemes, including extra guidance, reinforcement learning, audio-text self-supervised learning and diverse or controllable captioning. Effective data augmentation techniques, especially based on large language models are explored. Benchmark datasets and AAC-oriented evaluation metrics also accelerate the improvement of this field. This paper situates itself as a comprehensive survey covering the comparison between AAC and its related tasks, the existing deep learning techniques, datasets, and the evaluation metrics in AAC, with insights provided to guide potential future research directions.

翻译：自动音频描述（AAC）是一项模拟人类感知并创新性地连接音频处理与自然语言处理的任务，近年来取得了长足进步。AAC要求识别环境、声音事件及其时间关系等内容，并用流畅的句子描述这些元素。当前，基于编码器-解码器的深度学习框架是解决该问题的标准方法。大量研究提出了新颖的网络架构和训练方案，包括额外引导、强化学习、音频-文本自监督学习以及多样化/可控描述。基于大语言模型的有效数据增强技术也得到了探索。基准数据集和面向AAC的评估指标同样加速了该领域的发展。本文作为一份全面综述，涵盖了AAC与其相关任务的比较、现有深度学习技术、数据集及评估指标，并提供了指导未来潜在研究方向的见解。