  Audio captioning is a multi-modal task, focusing on using natural language
for describing the contents of general audio. Most audio captioning methods are
based on deep neural networks, employing an encoder-decoder scheme and a
dataset with audio clips and corresponding natural language descriptions (i.e.
captions). A significant challenge for audio captioning is the distribution of
words in the captions: some words are very frequent but acoustically
non-informative, i.e. the function words (e.g. "a", "the"), and other words are
infrequent but informative, i.e. the content words (e.g. adjectives, nouns). In
this paper we propose two methods to mitigate this class imbalance problem.
First, in an autoencoder setting for audio captioning, we weigh each word's
contribution to the training loss inversely proportional to its number of
occurrences in the whole dataset. Secondly, in addition to multi-class,
word-level audio captioning task, we define a multi-label side task based on
clip-level content word detection by training a separate decoder. We use the
loss from the second task to regularize the jointly trained encoder for the
audio captioning task. We evaluate our method using Clotho, a recently
published, wide-scale audio captioning dataset, and our results show an
increase of 37\% relative improvement with SPIDEr metric over the baseline
method.
