  Automated audio captioning (AAC) aims at generating summarizing descriptions
for audio clips. Multitudinous concepts are described in an audio caption,
ranging from local information such as sound events to global information like
acoustic scenery. Currently, the mainstream paradigm for AAC is the end-to-end
encoder-decoder architecture, expecting the encoder to learn all levels of
concepts embedded in the audio automatically. This paper first proposes a topic
model for audio descriptions, comprehensively analyzing the hierarchical audio
topics that are commonly covered. We then explore a transfer learning scheme to
access local and global information. Two source tasks are identified to
respectively represent local and global information, being Audio Tagging (AT)
and Acoustic Scene Classification (ASC). Experiments are conducted on the AAC
benchmark dataset Clotho and Audiocaps, amounting to a vast increase in all
eight metrics with topic transfer learning. Further, it is discovered that
local information and abstract representation learning are more crucial to AAC
than global information and temporal relationship learning.
