  Automated audio captioning (AAC) is a novel task, where a method takes as an
input an audio sample and outputs a textual description (i.e. a caption) of its
contents. Most AAC methods are adapted from from image captioning of machine
translation fields. In this work we present a novel AAC novel method,
explicitly focused on the exploitation of the temporal and time-frequency
patterns in audio. We employ three learnable processes for audio encoding, two
for extracting the local and temporal information, and one to merge the output
of the previous two processes. To generate the caption, we employ the widely
used Transformer decoder. We assess our method utilizing the freely available
splits of Clotho dataset. Our results increase previously reported highest
SPIDEr to 17.3, from 16.2.
