Abstract
Automated audio captioning (AAC) aims to generate textual descriptions for a given audio clip. Despite the existing AAC models obtaining promising performance, they struggle to capture intricate audio patterns due to only using a high-dimensional representation. In this paper, we propose a new encoder-decoder model for AAC, called the Pyramid Feature Fusion and Cross Context Attention Network (PFCA-Net). In PFCA-Net, the encoder is constructed using a pyramid network, facilitating the extraction of audio features across multiple scales. It achieves this by combining top-down and bottom-up connections to fuse features across scales, resulting in feature maps at various scales. In the decoder, cross-content attention is designed to fuse the different scale features which allows the propagation of information from a low-scale to a high-scale. Experimental results show that PFCA-Net achieves considerable improvement over existing models.