  One of the problems with automated audio captioning (AAC) is the
indeterminacy in word selection corresponding to the audio event/scene. Since
one acoustic event/scene can be described with several words, it results in a
combinatorial explosion of possible captions and difficulty in training. To
solve this problem, we propose a Transformer-based audio-captioning model with
keyword estimation called TRACKE. It simultaneously solves the word-selection
indeterminacy problem with the main task of AAC while executing the sub-task of
acoustic event detection/acoustic scene classification (i.e., keyword
estimation). TRACKE estimates keywords, which comprise a word set corresponding
to audio events/scenes in the input audio, and generates the caption while
referring to the estimated keywords to reduce word-selection indeterminacy.
Experimental results on a public AAC dataset indicate that TRACKE achieved
state-of-the-art performance and successfully estimated both the caption and
its keywords.
