• Corpus ID: 238634813

Improving the Performance of Automated Audio Captioning via Integrating the Acoustic and Semantic Information

@inproceedings{Ye2021ImprovingTP,
  title={Improving the Performance of Automated Audio Captioning via Integrating the Acoustic and Semantic Information},
  author={Zhongjie Ye and Helin Wang and Dongchao Yang and Yuexian Zou},
  booktitle={Workshop on Detection and Classification of Acoustic Scenes and Events},
  year={2021}
}
Automated audio captioning (AAC) has developed rapidly in recent years, involving acoustic signal processing and natural language processing to generate human-readable sentences for audio clips. The current models are generally based on the neural encoderdecoder architecture, and their decoder mainly uses acoustic information that is extracted from the CNN-based encoder. However, they have ignored semantic information that could help the AAC model to generate meaningful descriptions. This paper… 

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