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arXiv · 2110.06100

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

Zhongjie Ye·Helin Wang·Dongchao Yang·Yuexian Zou
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Abstract

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 encoder-decoder 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 proposes a novel approach for automated audio captioning based on incorporating semantic and acoustic information. Specifically, our audio captioning model consists of two sub-modules. (1) The pre-trained keyword encoder utilizes pre-trained ResNet38 to initialize its parameters, and then it is trained by extracted keywords as labels. (2) The multi-modal attention decoder adopts an LSTM-based decoder that contains semantic and acoustic attention modules. Experiments demonstrate that our proposed model achieves state-of-the-art performance on the Clotho dataset. Our code can be found at https://github.com/WangHelin1997/DCASE2021_Task6_PKU

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Zhongjie Ye, Helin Wang, Dongchao Yang, Yuexian Zou. 2021-10-12. Improving the Performance of Automated Audio Captioning via Integrating the Acoustic and Semantic Information. https://arxiv.org/abs/2110.06100

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