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Automated audio captioning (AAC), a task that mimics human perception as wellas innovatively links audio processing and natural language processing, hasoverseen much progress over the last few years. AAC requires recognizingcontents such as the environment, sound events and the temporal relationshipsbetween sound events and describing these elements with a fluent sentence.Currently, an encoder-decoder-based deep learning framework is the standardapproach to tackle this problem. Plenty of works have proposed novel networkarchitectures and training schemes, including extra guidance, reinforcementlearning, audio-text self-supervised learning and diverse or controllablecaptioning. Effective data augmentation techniques, especially based on largelanguage models are explored. Benchmark datasets and AAC-oriented evaluationmetrics also accelerate the improvement of this field. This paper situatesitself as a comprehensive survey covering the comparison between AAC and itsrelated tasks, the existing deep learning techniques, datasets, and theevaluation metrics in AAC, with insights provided to guide potential futureresearch directions.

Beyond the Status Quo: A Contemporary Survey of Advances and Challenges in Audio Captioning