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Automated audio captioning (AAC) has developed rapidly in recent years,involving acoustic signal processing and natural language processing togenerate human-readable sentences for audio clips. The current models aregenerally based on the neural encoder-decoder architecture, and their decodermainly uses acoustic information that is extracted from the CNN-based encoder.However, they have ignored semantic information that could help the AAC modelto generate meaningful descriptions. This paper proposes a novel approach forautomated audio captioning based on incorporating semantic and acousticinformation. Specifically, our audio captioning model consists of twosub-modules. (1) The pre-trained keyword encoder utilizes pre-trained ResNet38to initialize its parameters, and then it is trained by extracted keywords aslabels. (2) The multi-modal attention decoder adopts an LSTM-based decoder thatcontains semantic and acoustic attention modules. Experiments demonstrate thatour 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

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