  Automated audio captioning is multi-modal translation task that aim to
generate textual descriptions for a given audio clip. In this paper we propose
a full Transformer architecture that utilizes Patchout as proposed in [1],
significantly reducing the computational complexity and avoiding overfitting.
The caption generation is partly conditioned on textual AudioSet tags extracted
by a pre-trained classification model which is fine-tuned to maximize the
semantic similarity between AudioSet labels and ground truth captions. To
mitigate the data scarcity problem of Automated Audio Captioning we introduce
transfer learning from an upstream audio-related task and an enlarged in-domain
dataset. Moreover, we propose a method to apply Mixup augmentation for AAC.
Ablation studies are carried out to investigate how Patchout and text guidance
contribute to the final performance. The results show that the proposed
techniques improve the performance of our system and while reducing the
computational complexity. Our proposed method received the Judges Award at the
Task6A of DCASE Challenge 2022.
